Cross reality system with accurate shared maps

ABSTRACT

A cross reality system enables any of multiple devices to efficiently and accurately access previously persisted maps of very large scale environments and render virtual content specified in relation to those maps. The cross reality system may build a persisted map, which may be in canonical form, by merging tracking maps from the multiple devices. A map merge process determines mergibility of a tracking map with a canonical map and merges a tracking map with a canonical map in accordance with mergibility criteria, such as, when a gravity direction of the tracking map aligns with a gravity direction of the canonical map. Refraining from merging maps if the orientation of the tracking map with respect to gravity is not preserved avoids distortions in persisted maps and results in multiple devices, which may use the maps to determine their locations, to present more realistic and immersive experiences for their users.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/174,122, filed on Feb. 11, 2021, entitled “CROSS REALITY SYSTEM WITHACCURATE SHARED MAPS,” which claims priority to and the benefit under 35U.S.C. § 119(e) of U.S. Provisional Patent Application Ser. No.62/975,983, filed on Feb. 13, 2020, entitled “CROSS REALITY SYSTEM WITHACCURATE SHARED MAPS.” The contents of these applications are herebyincorporated herein by reference in their entirety.

TECHNICAL FIELD

This application relates generally to a cross reality system.

BACKGROUND

Computers may control human user interfaces to create a cross reality(XR) environment in which some or all of the XR environment, asperceived by the user, is generated by the computer. These XRenvironments may be virtual reality (VR), augmented reality (AR), andmixed reality (MR) environments, in which some or all of an XRenvironment may be generated by computers using, in part, data thatdescribes the environment. This data may describe, for example, virtualobjects that may be rendered in a way that users' sense or perceive as apart of a physical world and can interact with the virtual objects. Theuser may experience these virtual objects as a result of the data beingrendered and presented through a user interface device, such as, forexample, a head-mounted display device. The data may be displayed to theuser to see, or may control audio that is played for the user to hear,or may control a tactile (or haptic) interface, enabling the user toexperience touch sensations that the user senses or perceives as feelingthe virtual object.

XR systems may be useful for many applications, spanning the fields ofscientific visualization, medical training, engineering design andprototyping, tele-manipulation and tele-presence, and personalentertainment. AR and MR, in contrast to VR, include one or more virtualobjects in relation to real objects of the physical world. Theexperience of virtual objects interacting with real objects greatlyenhances the user's enjoyment in using the XR system, and also opens thedoor for a variety of applications that present realistic and readilyunderstandable information about how the physical world might bealtered.

To realistically render virtual content, an XR system may build arepresentation of the physical world around a user of the system. Thisrepresentation, for example, may be constructed by processing imagesacquired with sensors on a wearable device that forms a part of the XRsystem. In such a system, a user might perform an initialization routineby looking around a room or other physical environment in which the userintends to use the XR system until the system acquires sufficientinformation to construct a representation of that environment. As thesystem operates and the user moves around the environment or to otherenvironments, the sensors on the wearable devices might acquireadditional information to expand or update the representation of thephysical world.

BRIEF SUMMARY

Aspects of the present application relate to methods and apparatus forproviding cross reality (XR) scenes. Techniques as described herein maybe used together, separately, or in any suitable combination.

According to one embodiment, a method of merging one or more environmentmaps stored in a database with a tracking map computed based on sensordata collected by a device worn by a user is provided. The method maycomprise receiving the tracking map from the device, wherein thetracking map is aligned with respect to a gravity direction; determininga transformation between the tracking map and an environment map;determining whether to merge the environment map with the tracking map,wherein determining whether to merge comprises determining whetherapplying the transformation to the tracking map produces a transformedtracking map that aligns with respect to the gravity direction; andmerging the environment map with the tracking map based on determiningthat the transformed tracking map aligns with the gravity direction.

According to one embodiment, determining the transformation maycomprise, for corresponding features in the tracking map and theenvironment map, selecting as the determined transformation, thetransformation that aligns with the corresponding features with a metricof error below a threshold.

According to one embodiment, the method may further comprisingdetermining the corresponding features based on similarity ofidentifiers assigned to the features.

According to one embodiment, selecting as the determined transformationmay further comprise selecting the transformation that when appliedproduces the transformed tracking map that aligns with respect to thegravity direction.

According to one embodiment, determining the transformation may compriseapplying a plurality of candidate transformations to the tracking mapand selecting as the determined transformation a candidatetransformation of the plurality of candidate transformations.

According to one embodiment, selecting the determined transformation mayfurther comprise selecting as the determined transformation thecandidate transformation that when applied produces the transformedtracking map that aligns with respect to the gravity direction.

According to one embodiment, determining whether applying thetransformation to the tracking map produces the transformed tracking mapthat aligns with respect to the gravity direction comprises determiningwhether applying the transformation to the tracking map produces thetransformed tracking map that is rotated with respect to the gravitydirection by more than a threshold amount; in response to determiningthat applying the transformation to the tracking map does not producethe transformed tracking map that is rotated with respect to the gravitydirection by more than the threshold amount, selecting thetransformation as the determined transformation; and in response todetermining that applying the transformation to the tracking mapproduces the transformed tracking map that is rotated with respect tothe gravity direction by more than the threshold amount, discarding thetransformation.

According to one embodiment, the method may further comprise identifyinga set of environment maps from the database to be merged with thetracking map; and for each environment map in the set of environmentmaps: determining the transformation between the tracking map and theenvironment map; determining whether to merge the environment map withthe tracking map; and merging the environment map with the tracking mapbased on determining that the transformed tracking map aligns with thegravity direction.

According to one embodiment, the method may further comprise for eachenvironment map in the set of environment maps: refraining from mergingthe environment map with the tracking map based on determining that agravity direction of the environment map does not align with the gravitydirection of the transformed tracking map.

According to one embodiment, identifying the set of environment maps maycomprise determining area identifiers associated with the tracking map;and identifying the set of environment maps from the database based, atleast in part on, on the area identifiers associated with the trackingmap.

According to one embodiment, identifying the set of environment mapsfrom the database may further comprise filtering the set of environmentmaps based on similarity of one or more metrics associated with thetracking map and the environment maps in the set of environment maps.

According to one embodiment, a computing device is configured for use ina cross reality system in which a portable device operating in athree-dimensional (3D) environment renders virtual content. Thecomputing device may comprise at least one processor; acomputer-readable medium connected to the processor; a plurality ofenvironment maps stored in the computer-readable medium; andcomputer-executable instructions configured to, when executed by the atleast one processor, perform a method. The method performed by thecomputer-executable instructions may comprise receiving a tracking mapfrom the portable device, wherein the tracking map is aligned withrespect to a gravity direction; determining whether to merge anenvironment map with the tracking map, wherein determining whether tomerge comprises searching for a transformation of the tracking map thataligns a transformed tracking map and the environment map in a mannerthat preserves alignment of the transformed tracking map with respect tothe gravity direction; and merging the environment map with thetransformed tracking map based on determining that a gravity directionof the environment map aligns with the gravity direction of thetransformed tracking map.

According to one embodiment, searching for a transformation may comprisesearching for the transformation that aligns a first set of featuresassociated with the tracking map with a second set of featuresassociated with the environment map with a metric of error below athreshold.

According to one embodiment, searching for a transformation may comprisesearching for the transformation that does not change an orientation ofthe transformed tracking map with respect to the gravity direction.

According to one embodiment, searching for a transformation may comprisedetermining whether applying the transformation to the tracking mapproduces the transformed tracking map that is rotated with respect tothe gravity direction by more than a threshold amount; in response todetermining that applying the transformation to the tracking map doesnot produce the transformed tracking map that is rotated with respect tothe gravity direction by more than the threshold amount, applying thetransformation to the tracking map to generate the transformed trackingmap; and in response to determining that applying the transformation tothe tracking map produces the transformed tracking map that is rotatedwith respect to the gravity direction by more than the threshold amount,discarding the transformation.

According to one embodiment, the method may further comprise identifyinga set of environment maps from the plurality of environment maps to bemerged with the tracking map; and for each environment map in the set ofenvironment maps: determining whether to merge the environment map withthe tracking map; and merging the environment map with the transformedtracking map based on determining that a gravity direction of theenvironment map aligns with the gravity direction of the transformedtracking map.

According to one embodiment, the method may further comprise, for eachenvironment map in the set of environment maps: refraining from mergingthe environment map with the transformed tracking map based ondetermining that the gravity direction of the environment map does notalign with the gravity direction of the tracking map.

According to one embodiment, identifying the set of environment maps mayfurther comprise determining area identifiers associated with thetracking map; and identifying the set of environment maps based, atleast in part on, on the area identifiers associated with the trackingmap.

According to one embodiment, identifying the set of environment maps mayfurther comprise filtering the set of environment maps based onsimilarity of one or more metrics associated with the tracking map andthe environment maps in the set of environment maps.

According to one embodiment, a cloud computing environment for anaugmented reality system is configured for communication with aplurality of user devices comprising sensors. The cloud computingenvironment for an augmented reality system may comprise a map databasestoring a plurality of environment maps constructed from data suppliedby the plurality of user devices; and non-transitory computer storagemedia storing computer-executable instructions that, when executed by atleast one processor in the cloud computing environment, perform amethod. The method may comprise receiving a tracking map from a userdevice, wherein the tracking map is aligned with respect to a gravitydirection; updating the map database based on the received tracking map,wherein updating the map database comprises, for each environment map ina set of environment maps: determining a transformation between thetracking map and the environment map; determining whether to merge theenvironment map with the tracking map, wherein determining whether tomerge comprises determining whether applying the transformation to thetracking map produced a transformed tracking map that aligns withrespect to the gravity direction; and merging the environment map withthe tracking map based on determining that the transformed tracking mapaligns with the gravity direction.

According to one embodiment, determining the transformation maycomprise, for corresponding features in the tracking map and theenvironment map, selecting as the determined transformation, thetransformation that aligns the corresponding features with a metric oferror below a threshold.

According to one embodiment, the method may further comprise determiningthe corresponding features based on similarity of identifiers assignedto the features.

According to one embodiment, selecting as the determined transformationmay further comprise selecting the transformation that when appliedproduces the transformed tracking map that aligns with respect to thegravity direction.

According to one embodiment, determining the transformation may compriseapplying a plurality of candidate transformations to the tracking mapand selecting as the determined transformation a candidatetransformation of the plurality of candidate transformations.

According to one embodiment, selecting as the determined transformationmay further comprise selecting as the determined transformation thecandidate transformation that when applied produces the transformedtracking map that aligns with respect to the gravity direction.

According to one embodiment, determining whether applying thetransformation to the tracking map produces the transformed tracking mapthat aligns with respect to the gravity direction may comprise:determining whether applying the transformation to the tracking mapproduces the transformed tracking map that is rotated with respect tothe gravity direction by more than a threshold amount; in response todetermining that applying the transformation to the tracking map doesnot produce the transformed tracking map that is rotated with respect tothe gravity direction by more than the threshold amount, selecting thetransformation as the determined transformation; and in response todetermining that applying the transformation to the tracking mapproduces the transformed tracking map that is rotated with respect tothe gravity direction by more than the threshold amount, discarding thetransformation.

The foregoing summary is provided by way of illustration and is notintended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not intended to be drawn to scale. In thedrawings, each identical or nearly identical component that isillustrated in various figures is represented by a like numeral. Forpurposes of clarity, not every component may be labeled in everydrawing. In the drawings:

FIG. 1 is a sketch illustrating an example of a simplified augmentedreality (AR) scene, according to some embodiments;

FIG. 2 is a sketch of an exemplary simplified AR scene, showingexemplary use cases of an XR system, according to some embodiments;

FIG. 3 is a schematic diagram illustrating data flow for a single userin an AR system configured to provide an experience to the user of ARcontent interacting with a physical world, according to someembodiments;

FIG. 4 is a schematic diagram illustrating an exemplary AR displaysystem, displaying virtual content for a single user, according to someembodiments;

FIG. 5A is a schematic diagram illustrating a user wearing an AR displaysystem rendering AR content as the user moves through a physical worldenvironment, according to some embodiments;

FIG. 5B is a schematic diagram illustrating a viewing optics assemblyand attendant components, according to some embodiments;

FIG. 6A is a schematic diagram illustrating an AR system using a worldreconstruction system, according to some embodiments;

FIG. 6B is a schematic diagram illustrating components of an AR systemthat maintain a model of a passable world, according to someembodiments;

FIG. 7 is a schematic illustration of a tracking map formed by a devicetraversing a path through a physical world;

FIG. 8 is a schematic diagram illustrating a user of a cross reality(XR) system, perceiving virtual content, according to some embodiments;

FIG. 9 is a block diagram of components of a first XR device of the XRsystem of FIG. 8 that transform between coordinate systems, according tosome embodiments;

FIG. 10 is a schematic diagram illustrating an exemplary transformationof origin coordinate frames into destination coordinate frames in orderto correctly render local XR content, according to some embodiments;

FIG. 11 is a top plan view illustrating pupil-based coordinate frames,according to some embodiments;

FIG. 12 is a top plan view illustrating a camera coordinate frame thatincludes all pupil positions, according to some embodiments;

FIG. 13 is a schematic diagram of the display system of FIG. 9 ,according to some embodiments;

FIG. 14 is a block diagram illustrating the creation of a persistentcoordinate frame (PCF) and the attachment of XR content to the PCF,according to some embodiments;

FIG. 15 is a flow chart illustrating a method of establishing and usinga PCF, according to some embodiments;

FIG. 16 is a block diagram of the XR system of FIG. 8 , including asecond XR device, according to some embodiments;

FIG. 17 is a schematic diagram illustrating a room and key frames thatare established for various areas in the room, according to someembodiments;

FIG. 18 is a schematic diagram illustrating the establishment ofpersistent poses based on the key frames, according to some embodiments;

FIG. 19 is a schematic diagram illustrating the establishment of apersistent coordinate frame (PCF) based on the persistent poses,according to some embodiments;

FIGS. 20A to 20C are schematic diagrams illustrating an example ofcreating PCFs, according to some embodiments;

FIG. 21 is a block diagram illustrating a system for generating globaldescriptors for individual images and/or maps, according to someembodiments;

FIG. 22 is a flow chart illustrating a method of computing an imagedescriptor, according to some embodiments;

FIG. 23 is a flow chart illustrating a method of localization usingimage descriptors, according to some embodiments;

FIG. 24 is a flow chart illustrating a method of training a neuralnetwork, according to some embodiments;

FIG. 25 is a block diagram illustrating a method of training a neuralnetwork, according to some embodiments;

FIG. 26 is a schematic diagram illustrating an AR system configured torank and merge a plurality of environment maps, according to someembodiments;

FIG. 27 is a simplified block diagram illustrating a plurality ofcanonical maps stored on a remote storage medium, according to someembodiments;

FIG. 28 is a schematic diagram illustrating a method of selectingcanonical maps to, for example, localize a new tracking map in one ormore canonical maps and/or obtain PCFs from the canonical maps,according to some embodiments;

FIG. 29 is flow chart illustrating a method of selecting a plurality ofranked environment maps, according to some embodiments;

FIG. 30 is a schematic diagram illustrating an exemplary map rankportion of the AR system of FIG. 26 , according to some embodiments;

FIG. 31A is a schematic diagram illustrating an example of areaattributes of a tracking map (TM) and environment maps in a database,according to some embodiments;

FIG. 31B is a schematic diagram illustrating an example of determining ageographic location of a tracking map (TM) for geolocation filtering ofFIG. 29 , according to some embodiments;

FIG. 32 is a schematic diagram illustrating an example of geolocationfiltering of FIG. 29 , according to some embodiments;

FIG. 33 is a schematic diagram illustrating an example of Wi-Fi BSSIDfiltering of FIG. 29 , according to some embodiments;

FIG. 34 is a schematic diagram illustrating an example of use oflocalization of FIG. 29 , according to some embodiments;

FIGS. 35 and 36 are block diagrams of an XR system configured to rankand merge a plurality of environment maps, according to someembodiments.

FIG. 37 is a block diagram illustrating a method of creating environmentmaps of a physical world, in a canonical form, according to someembodiments;

FIGS. 38A and 38B are schematic diagrams illustrating an environment mapcreated in a canonical form by updating the tracking map of FIG. 7 witha new tracking map, according to some embodiments.

FIGS. 39A to 39F are schematic diagrams illustrating an example ofmerging maps, according to some embodiments;

FIG. 40 is a two-dimensional representation of a three-dimensional firstlocal tracking map (Map 1), which may be generated by the first XRdevice of FIG. 9 , according to some embodiments;

FIG. 41 is a block diagram illustrating uploading Map 1 from the firstXR device to the server of FIG. 9 , according to some embodiments;

FIG. 42 is a schematic diagram illustrating the XR system of FIG. 16 ,showing the second user has initiated a second session using a second XRdevice of the XR system after the first user has terminated a firstsession, according to some embodiments;

FIG. 43A is a block diagram illustrating a new session for the second XRdevice of FIG. 42 , according to some embodiments;

FIG. 43B is a block diagram illustrating the creation of a tracking mapfor the second XR device of FIG. 42 , according to some embodiments;

FIG. 43C is a block diagram illustrating downloading a canonical mapfrom the server to the second XR device of FIG. 42 , according to someembodiments;

FIG. 44 is a schematic diagram illustrating a localization attempt tolocalize to a canonical map a second tracking map (Map 2), which may begenerated by the second XR device of FIG. 42 , according to someembodiments;

FIG. 45 is a schematic diagram illustrating a localization attempt tolocalize to a canonical map the second tracking map (Map 2) of FIG. 44 ,which may be further developed and with XR content associated with PCFsof Map 2, according to some embodiments;

FIGS. 46A-46B are a schematic diagram illustrating a successfullocalization of Map 2 of FIG. 45 to the canonical map, according to someembodiments;

FIG. 47 is a schematic diagram illustrating a canonical map generated byincluding one or more PCFs from the canonical map of FIG. 46A into Map 2of FIG. 45 , according to some embodiments;

FIG. 48 is a schematic diagram illustrating the canonical map of FIG. 47with further expansion of Map 2 on the second XR device, according tosome embodiments;

FIG. 49 is a block diagram illustrating uploading Map 2 from the secondXR device to the server, according to some embodiments;

FIG. 50 is a block diagram illustrating merging Map 2 with the canonicalmap, according to some embodiments;

FIG. 51 is a block diagram illustrating transmission of a new canonicalmap from the server to the first and second XR devices, according tosome embodiments;

FIG. 52 is a block diagram illustrating a two-dimensional representationof Map 2 and a head coordinate frame of the second XR device that isreferenced to Map 2, according to some embodiments;

FIG. 53 is a block diagram illustrating, in two-dimensions, adjustmentof the head coordinate frame which can occur in six degrees of freedom,according to some embodiments;

FIG. 54 is a block diagram illustrating a canonical map on the second XRdevice wherein sound is localized relative to PCFs of Map 2, accordingto some embodiments;

FIGS. 55 and 56 are a perspective view and a block diagram illustratinguse of the XR system when the first user has terminated a first sessionand the first user has initiated a second session using the XR system,according to some embodiments;

FIGS. 57 and 58 are a perspective view and a block diagram illustratinguse of the XR system when three users are simultaneously using the XRsystem in the same session, according to some embodiments;

FIG. 59 is a flow chart illustrating a method of recovering andresetting a headpose, according to some embodiments;

FIG. 60 is a block diagram of a machine in the form of a computer thatcan find application in the present invention system, according to someembodiments;

FIG. 61 is a schematic diagram of an example XR system in which any ofmultiple devices may access a localization service, according to someembodiments;

FIG. 62 is an example process flow for operation of a portable device aspart of an XR system that provides cloud-based localization, accordingto some embodiments;

FIGS. 63A, B, and C are an example process flow for cloud-basedlocalization, according to some embodiments;

FIG. 64 is a block diagram of an XR system that provides large scalelocalization, according to some embodiments;

FIG. 65 is a schematic diagram illustrating information about a physicalworld being processed by the XR system of FIG. 64 , according to someembodiments;

FIG. 66 is a block diagram of a subsystem of the XR system of FIG. 64including the matched correspondences quality predication component anda pose estimation component, according to some embodiments;

FIG. 67 is a flow chart illustrating a method of generating dataset fortraining the subsystem of FIG. 66 , according to some embodiments;

FIG. 68 is a flow chart illustrating a method of training the subsystemof FIG. 66 , according to some embodiments; and

FIG. 69 illustrates a gravity-preserving map merge process, according tosome embodiments.

DETAILED DESCRIPTION

Described herein are methods and apparatus for providing XR scenes. Toprovide realistic XR experiences to multiple users, an XR system mustknow the users' location within the physical world in order to correctlycorrelate locations of virtual objects in relation to real objects. Theinventors have recognized and appreciated methods and apparatus thatlocalize XR devices in large and very large scale environments (e.g., aneighborhood, a city, a country, the globe) with reduced time andimproved accuracy.

An XR system may build an environment map of a scene, which may becreated from image and/or depth information collected with sensors thatare part of XR devices worn by users of the XR system. Each XR devicemay develop a local map of its physical environment by integratinginformation from one or more images collected as the device operates. Insome embodiments, the coordinate system of that map is tied to theorientation of the device when the device initiated scanning thephysical world. That orientation may change from session to session as auser interacts with the XR system, whether different sessions areassociated with different users, each with their own wearable devicewith sensors that scan the environment, or the same user who uses thesame device at different times.

The XR system may implement one or more techniques so as to enableoperation based on persistent spatial information. The techniques, forexample, may provide XR scenes for a more computationally efficient andimmersive experience for a single or multiple users by enablingpersistent spatial information to be created, stored, and retrieved byany of multiple users of an XR system. Persistent spatial informationmay also enable quickly recovering and resetting headposes on each ofone or more XR devices in a computationally efficient way.

The persistent spatial information may be represented by a persistentmap. The persistent map may be stored in a remote storage medium (e.g.,a cloud). For example, the wearable device worn by a user, after beingturned on, may retrieve from persistent storage, such as from cloudstorage, an appropriate map that was previously created and stored. Thatpreviously stored map may have been based on data about the environmentcollected with sensors on the user's wearable device during priorsessions. Retrieving a stored map may enable use of the wearable devicewithout completing a scan of the physical world with the sensors on thewearable device. Alternatively or additionally, the system/device, uponentering a new region of the physical world, may similarly retrieve anappropriate stored map.

The stored map may be represented in a canonical form to which a localframe of reference on each XR device may be related. In a multidevice XRsystem, the stored map accessed by one device may have been created andstored by another device and/or may have been constructed by aggregatingdata about the physical world collected by sensors on multiple wearabledevices that were previously present in at least a portion of thephysical world represented by the stored map.

In some embodiments, persistent spatial information may be representedin a way that may be readily shared among users and among thedistributed components, including applications. Canonical maps mayprovide information about the physical world, for example, as persistentcoordinate frames (PCFs). A PCF may be defined based on a set offeatures recognized in the physical world. The features may be selectedsuch that they are likely to be the same from user session to usersession of the XR system. PCFs may exist sparsely, providing less thanall of the available information about the physical world, such thatthey may be efficiently processed and transferred. Techniques forprocessing persistent spatial information may include creating dynamicmaps based on the local coordinate systems of one or more devices acrossone or more sessions. These maps may be sparse maps, representing thephysical world based on a subset of the feature points detected inimages used in forming the maps. The persistent coordinate frames (PCF)may be generated from the sparse maps, and may be exposed to XRapplications via, for example, an application programming interface(API). These capabilities may be supported by techniques for forming thecanonical maps by merging multiple maps created by one or more XRdevices.

The relationship between the canonical map and a local map for eachdevice may be determined through a localization process. Thatlocalization process may be performed on each XR device based on a setof canonical maps selected and sent to the device. Alternatively oradditionally, a localization service may be provided on remoteprocessors, such as might be implemented in the cloud.

Sharing data about the physical world among multiple devices may enableshared user experiences of virtual content. Two XR devices that haveaccess to the same stored map, for example, may both localize withrespect to the stored map. Once localized, a user device may rendervirtual content that has a location specified by reference to the storedmap by translating that location to a frame of reference maintained bythe user device. The user device may use this local frame of referenceto control the display of the user device to render the virtual contentin the specified location.

To support these and other functions, the XR system may includecomponents that, based on data about the physical world collected withsensors on user devices, develop, maintain, and use persistent spatialinformation, including one or more stored maps. These components may bedistributed across the XR system, with some operating, for example, on ahead mounted portion of a user device. Other components may operate on acomputer, associated with the user coupled to the head mounted portionover a local or personal area network. Yet others may operate at aremote location, such as at one or more servers accessible over a widearea network.

These components, for example, may include components that can identifyfrom information about the physical world collected by one or more userdevices information that is of sufficient quality to be stored as or ina persistent map. An example of such a component, described in greaterdetail below, is a map merge component. Such a component, for example,may receive inputs from a user device and determine the suitability ofparts of the inputs to be used to update a persistent map, which may bein canonical form. A map merge component, for example, may also promotea local map from a user device that is not merged with a persistent mapto be a separate persistent map.

As another example, these components may include components that may aidin selecting an appropriate set of one or more persistent maps thatlikely represent the same region of the physical world as is representedby location information provided by a user device. An example of suchcomponents, described in greater detail below are map rank and mapselect components. Such components, for example, may receive inputs froma user device and identify one or more persistent maps that are likelyto represent the region of the physical world in which that device isoperating. A map rank component, for example, may aid in selecting apersistent map to be used by that local device as it renders virtualcontent, gathers data about the environment, or performs other actions.A map rank component, alternatively or additionally, may aid inidentifying persistent maps to be updated as additional informationabout the physical world is collected by one or more user devices.

Yet other components may determine transformations that transforminformation captured or described in relation to one reference frameinto another reference frame. For example, sensors may be attached to ahead mounted display such that the data read from those sensorsindicates locations of objects in the physical world with respect to theheadpose of the wearer. One or more transformations may be applied torelate that location information to the coordinate frame associated witha persistent environment map. Similarly, data indicating where a virtualobject is to be rendered when expressed in a coordinate frame of apersistent environment map may be put through one or moretransformations to be in a frame of reference of the display on theuser's head. As described in greater detail below, there may be multiplesuch transformations. These transformations may be partitioned acrossthe components of an XR system such that they may be efficiently updatedand or applied in a distributed system.

In some embodiments, the persistent maps may be constructed frominformation collected by multiple user devices. The XR devices may eachcapture local spatial information and construct separate tracking mapswith information collected by sensors of each of the XR devices atvarious locations and times. Each tracking map may include points, eachof which may be associated with a feature of a real object that mayinclude multiple features. In addition to potentially supplying input tocreate and maintain persistent maps, the tracking maps may be used totrack users' motions in a scene, enabling an XR system to estimaterespective users' headposes relative to the frame of referenceestablished by the tracking map on that user's device.

This co-dependence between the creation of a map and the estimation ofheadpose constitutes significant challenges. Substantial processing maybe required to create the map and estimate headposes simultaneously. Theprocessing must be accomplished quickly as objects move in the scene(e.g., moving a cup on a table) and as users move in the scene becauselatency makes XR experiences less realistic for users. On the otherhand, an XR device can provide limited computational resources becausean XR device should be lightweight for a user to wear comfortably. Lackof computational resources cannot be compensated for with more sensors,as adding sensors would also undesirably add weight. Further, eithermore sensors or more computational resources leads to heat, which maycause deformation of an XR device.

The XR system may be configured to create, share, and use persistentspatial information with low usage of computational resources and/or lowlatency to provide a more immersive user experience. Some suchtechniques may enable efficient comparison of spatial information. Suchcomparisons may arise, for example, as part of localization in which aset of features from a local device is matched to a set of features in acanonical map.

Similarly, in a map merge process, attempts may be made to match one ormore sets of features in a tracking map from a device to correspondingfeatures in a canonical map and determine a transformation between thesets of corresponding features that provides a suitably low errorbetween the positions of transformed features in a first set of featuresderived from the tracking map and a second set of features derived fromthe canonical map. Subsequent processing to incorporate the tracking mapinto a set of canonical maps may be based on the results of thatcomparison. For example, determining a transformation with suitably lowerror may indicate that a region represented by the second set offeatures derived from the canonical map correspond to a same regionrepresented by the first set of features derived from the tracking mapand that the two maps can be merged.

The inventors have recognized that errors may, nonetheless, beintroduced in the merging process even when there is low error in thealignment of the sets of features. The inventors have further recognizedand appreciated that such errors may be detected by a transformationthat changes the orientation of the tracking map with respect to agravity direction, and that merging such a tracking map with thecanonical map may result in a skewed merged map.

By suppressing the merging of transformed tracking maps that have anorientation changed with respect to gravity, the canonical map may startwith, and retain an alignment with respect to gravity. Merging errorscan be reduced by ensuring that a gravity direction of a transformedtracking map (i.e., after applying the determined transformation) alignswith a gravity direction of the canonical map with which the trackingmap is to be merged.

Techniques as described herein may be used together or separately withmany types of devices and for many types of scenes, including wearableor portable devices with limited computational resources that provide anaugmented or mixed reality scene. In some embodiments, the techniquesmay be implemented by one or more services that form a portion of an XRsystem.

AR System Overview

FIGS. 1 and 2 illustrate scenes with virtual content displayed inconjunction with a portion of the physical world. For purposes ofillustration, an AR system is used as an example of an XR system. FIGS.3-6B illustrate an exemplary AR system, including one or moreprocessors, memory, sensors and user interfaces that may operateaccording to the techniques described herein.

Referring to FIG. 1 , an outdoor AR scene 354 is depicted in which auser of an AR technology sees a physical world park-like setting 356,featuring people, trees, buildings in the background, and a concreteplatform 358. In addition to these items, the user of the AR technologyalso perceives that they “see” a robot statue 357 standing upon thephysical world concrete platform 358, and a cartoon-like avatarcharacter 352 flying by which seems to be a personification of a bumblebee, even though these elements (e.g., the avatar character 352, and therobot statue 357) do not exist in the physical world. Due to the extremecomplexity of the human visual perception and nervous system, it ischallenging to produce an AR technology that facilitates a comfortable,natural-feeling, rich presentation of virtual image elements amongstother virtual or physical world imagery elements.

Such an AR scene may be achieved with a system that builds maps of thephysical world based on tracking information, enable users to place ARcontent in the physical world, determine locations in the maps of thephysical world where AR content are placed, preserve the AR scenes suchthat the placed AR content can be reloaded to display in the physicalworld during, for example, a different AR experience session, and enablemultiple users to share an AR experience. The system may build andupdate a digital representation of the physical world surfaces aroundthe user. This representation may be used to render virtual content soas to appear fully or partially occluded by physical objects between theuser and the rendered location of the virtual content, to place virtualobjects, in physics based interactions, and for virtual character pathplanning and navigation, or for other operations in which informationabout the physical world is used.

FIG. 2 depicts another example of an indoor AR scene 400, showingexemplary use cases of an XR system, according to some embodiments. Theexemplary scene 400 is a living room having walls, a bookshelf on oneside of a wall, a floor lamp at a corner of the room, a floor, a sofa,and coffee table on the floor. In addition to these physical items, theuser of the AR technology also perceives virtual objects such as imageson the wall behind the sofa, birds flying through the door, a deerpeeking out from the book shelf, and a decoration in the form of awindmill placed on the coffee table.

For the images on the wall, the AR technology requires information aboutnot only surfaces of the wall but also objects and surfaces in the roomsuch as lamp shape, which are occluding the images to render the virtualobjects correctly. For the flying birds, the AR technology requiresinformation about all the objects and surfaces around the room forrendering the birds with realistic physics to avoid the objects andsurfaces or bounce off them if the birds collide. For the deer, the ARtechnology requires information about the surfaces such as the floor orcoffee table to compute where to place the deer. For the windmill, thesystem may identify that is an object separate from the table and maydetermine that it is movable, whereas corners of shelves or corners ofthe wall may be determined to be stationary. Such a distinction may beused in determinations as to which portions of the scene are used orupdated in each of various operations.

The virtual objects may be placed in a previous AR experience session.When new AR experience sessions start in the living room, the ARtechnology requires the virtual objects being accurately displayed atthe locations previously placed and realistically visible from differentviewpoints. For example, the windmill should be displayed as standing onthe books rather than drifting above the table at a different locationwithout the books. Such drifting may happen if the locations of theusers of the new AR experience sessions are not accurately localized inthe living room. As another example, if a user is viewing the windmillfrom a viewpoint different from the viewpoint when the windmill wasplaced, the AR technology requires corresponding sides of the windmillbeing displayed.

A scene may be presented to the user via a system that includes multiplecomponents, including a user interface that can stimulate one or moreuser senses, such as sight, sound, and/or touch. In addition, the systemmay include one or more sensors that may measure parameters of thephysical portions of the scene, including position and/or motion of theuser within the physical portions of the scene. Further, the system mayinclude one or more computing devices, with associated computerhardware, such as memory. These components may be integrated into asingle device or may be distributed across multiple interconnecteddevices. In some embodiments, some or all of these components may beintegrated into a wearable device.

FIG. 3 depicts an AR system 502 configured to provide an experience ofAR contents interacting with a physical world 506, according to someembodiments. The AR system 502 may include a display 508. In theillustrated embodiment, the display 508 may be worn by the user as partof a headset such that a user may wear the display over their eyes likea pair of goggles or glasses. At least a portion of the display may betransparent such that a user may observe a see-through reality 510. Thesee-through reality 510 may correspond to portions of the physical world506 that are within a present viewpoint of the AR system 502, which maycorrespond to the viewpoint of the user in the case that the user iswearing a headset incorporating both the display and sensors of the ARsystem to acquire information about the physical world.

AR contents may also be presented on the display 508, overlaid on thesee-through reality 510. To provide accurate interactions between ARcontents and the see-through reality 510 on the display 508, the ARsystem 502 may include sensors 522 configured to capture informationabout the physical world 506.

The sensors 522 may include one or more depth sensors that output depthmaps 512. Each depth map 512 may have multiple pixels, each of which mayrepresent a distance to a surface in the physical world 506 in aparticular direction relative to the depth sensor. Raw depth data maycome from a depth sensor to create a depth map. Such depth maps may beupdated as fast as the depth sensor can form a new image, which may behundreds or thousands of times per second. However, that data may benoisy and incomplete, and have holes shown as black pixels on theillustrated depth map.

The system may include other sensors, such as image sensors. The imagesensors may acquire monocular or stereoscopic information that may beprocessed to represent the physical world in other ways. For example,the images may be processed in world reconstruction component 516 tocreate a mesh, representing connected portions of objects in thephysical world. Metadata about such objects, including for example,color and surface texture, may similarly be acquired with the sensorsand stored as part of the world reconstruction.

The system may also acquire information about the headpose (or “pose”)of the user with respect to the physical world. In some embodiments, aheadpose tracking component of the system may be used to computeheadposes in real time. The headpose tracking component may represent aheadpose of a user in a coordinate frame with six degrees of freedomincluding, for example, translation in three perpendicular axes (e.g.,forward/backward, up/down, left/right) and rotation about the threeperpendicular axes (e.g., pitch, yaw, and roll). In some embodiments,sensors 522 may include inertial measurement units that may be used tocompute and/or determine a headpose 514. A headpose 514 for a depth mapmay indicate a present viewpoint of a sensor capturing the depth mapwith six degrees of freedom, for example, but the headpose 514 may beused for other purposes, such as to relate image information to aparticular portion of the physical world or to relate the position ofthe display worn on the user's head to the physical world.

In some embodiments, the headpose information may be derived in otherways than from an IMU, such as from analyzing objects in an image. Forexample, the headpose tracking component may compute relative positionand orientation of an AR device to physical objects based on visualinformation captured by cameras and inertial information captured byIMUs. The headpose tracking component may then compute a headpose of theAR device by, for example, comparing the computed relative position andorientation of the AR device to the physical objects with features ofthe physical objects. In some embodiments, that comparison may be madeby identifying features in images captured with one or more of thesensors 522 that are stable over time such that changes of the positionof these features in images captured over time can be associated with achange in headpose of the user.

The inventors have realized and appreciated techniques for operating XRsystems to provide XR scenes for a more immersive user experience suchas estimating headpose at a frequency of 1 kHz, with low usage ofcomputational resources in connection with an XR device, that may beconfigured with, for example, four video graphic array (VGA) camerasoperating at 30 Hz, one inertial measurement unit (IMU) operating at 1kHz, compute power of a single advanced RISC machine (ARM) core, memoryless than 1 GB, and network bandwidth less than 100 Mbp. Thesetechniques relate to reducing processing required to generate andmaintain maps and estimate headpose as well as to providing andconsuming data with low computational overhead. The XR system maycalculate its pose based on the matched visual features. U.S. patentapplication Ser. No. 16/221,065 describes hybrid tracking and is herebyincorporated herein by reference in its entirety.

In some embodiments, the AR device may construct a map from the featurepoints recognized in successive images in a series of image framescaptured as a user moves throughout the physical world with the ARdevice. Though each image frame may be taken from a different pose asthe user moves, the system may adjust the orientation of the features ofeach successive image frame to match the orientation of the initialimage frame by matching features of the successive image frames topreviously captured image frames. Translations of the successive imageframes so that points representing the same features will matchcorresponding feature points from previously collected image frames, canbe used to align each successive image frame to match the orientation ofpreviously processed image frames. The frames in the resulting map mayhave a common orientation established when the first image frame wasadded to the map. This map, with sets of feature points in a commonframe of reference, may be used to determine the user's pose within thephysical world by matching features from current image frames to themap. In some embodiments, this map may be called a tracking map.

In addition to enabling tracking of the user's pose within theenvironment, this map may enable other components of the system, such asworld reconstruction component 516, to determine the location ofphysical objects with respect to the user. The world reconstructioncomponent 516 may receive the depth maps 512 and headposes 514, and anyother data from the sensors, and integrate that data into areconstruction 518. The reconstruction 518 may be more complete and lessnoisy than the sensor data. The world reconstruction component 516 mayupdate the reconstruction 518 using spatial and temporal averaging ofthe sensor data from multiple viewpoints over time.

The reconstruction 518 may include representations of the physical worldin one or more data formats including, for example, voxels, meshes,planes, etc. The different formats may represent alternativerepresentations of the same portions of the physical world or mayrepresent different portions of the physical world. In the illustratedexample, on the left side of the reconstruction 518, portions of thephysical world are presented as a global surface; on the right side ofthe reconstruction 518, portions of the physical world are presented asmeshes.

In some embodiments, the map maintained by headpose component 514 may besparse relative to other maps that might be maintained of the physicalworld. Rather than providing information about locations, and possiblyother characteristics, of surfaces, the sparse map may indicatelocations of interest points and/or structures, such as corners oredges. In some embodiments, the map may include image frames as capturedby the sensors 522. These frames may be reduced to features, which mayrepresent the interest points and/or structures. In conjunction witheach frame, information about a pose of a user from which the frame wasacquired may also be stored as part of the map. In some embodiments,every image acquired by the sensor may or may not be stored. In someembodiments, the system may process images as they are collected bysensors and select subsets of the image frames for further computation.The selection may be based on one or more criteria that limits theaddition of information yet ensures that the map contains usefulinformation. The system may add a new image frame to the map, forexample, based on overlap with a prior image frame already added to themap or based on the image frame containing a sufficient number offeatures determined as likely to represent stationary objects. In someembodiments, the selected image frames, or groups of features fromselected image frames may serve as key frames for the map, which areused to provide spatial information.

In some embodiments, the amount of data that is processed whenconstructing maps may be reduced, such as by constructing sparse mapswith a collection of mapped points and keyframes and/or dividing themaps into blocks to enable updates by blocks. A mapped point may beassociated with a point of interest in the environment. A keyframe mayinclude selected information from camera-captured data. U.S. patentapplication Ser. No. 16/520,582 describes determining and/or evaluatinglocalization maps and is hereby incorporated herein by reference in itsentirety.

The AR system 502 may integrate sensor data over time from multipleviewpoints of a physical world. The poses of the sensors (e.g., positionand orientation) may be tracked as a device including the sensors ismoved. As the sensor's frame pose is known and how it relates to theother poses, each of these multiple viewpoints of the physical world maybe fused together into a single, combined reconstruction of the physicalworld, which may serve as an abstract layer for the map and providespatial information. The reconstruction may be more complete and lessnoisy than the original sensor data by using spatial and temporalaveraging (i.e. averaging data from multiple viewpoints over time), orany other suitable method.

In the illustrated embodiment in FIG. 3 , a map represents the portionof the physical world in which a user of a single, wearable device ispresent. In that scenario, headpose associated with frames in the mapmay be represented as a local headpose, indicating orientation relativeto an initial orientation for a single device at the start of a session.For example, the headpose may be tracked relative to an initial headposewhen the device was turned on or otherwise operated to scan anenvironment to build a representation of that environment.

In combination with content characterizing that portion of the physicalworld, the map may include metadata. The metadata, for example, mayindicate time of capture of the sensor information used to form the map.Metadata alternatively or additionally may indicate location of thesensors at the time of capture of information used to form the map.Location may be expressed directly, such as with information from a GPSchip, or indirectly, such as with a wireless (e.g. Wi-Fi) signatureindicating strength of signals received from one or more wireless accesspoints while the sensor data was being collected and/or withidentifiers, such as BSSID's, of wireless access points to which theuser device connected while the sensor data was collected.

The reconstruction 518 may be used for AR functions, such as producing asurface representation of the physical world for occlusion processing orphysics-based processing. This surface representation may change as theuser moves or objects in the physical world change. Aspects of thereconstruction 518 may be used, for example, by a component 520 thatproduces a changing global surface representation in world coordinates,which may be used by other components.

The AR content may be generated based on this information, such as by ARapplications 504. An AR application 504 may be a game program, forexample, that performs one or more functions based on information aboutthe physical world, such as visual occlusion, physics-basedinteractions, and environment reasoning. It may perform these functionsby querying data in different formats from the reconstruction 518produced by the world reconstruction component 516. In some embodiments,component 520 may be configured to output updates when a representationin a region of interest of the physical world changes. That region ofinterest, for example, may be set to approximate a portion of thephysical world in the vicinity of the user of the system, such as theportion within the view field of the user, or is projected(predicted/determined) to come within the view field of the user.

The AR applications 504 may use this information to generate and updatethe AR contents. The virtual portion of the AR contents may be presentedon the display 508 in combination with the see-through reality 510,creating a realistic user experience.

In some embodiments, an AR experience may be provided to a user throughan XR device, which may be a wearable display device, which may be partof a system that may include remote processing and or remote datastorage and/or, in some embodiments, other wearable display devices wornby other users. FIG. 4 illustrates an example of system 580 (hereinafterreferred to as “system 580”) including a single wearable device forsimplicity of illustration. The system 580 includes a head mounteddisplay device 562 (hereinafter referred to as “display device 562”),and various mechanical and electronic modules and systems to support thefunctioning of the display device 562. The display device 562 may becoupled to a frame 564, which is wearable by a display system user orviewer 560 (hereinafter referred to as “user 560”) and configured toposition the display device 562 in front of the eyes of the user 560.According to various embodiments, the display device 562 may be asequential display. The display device 562 may be monocular orbinocular. In some embodiments, the display device 562 may be an exampleof the display 508 in FIG. 3 .

In some embodiments, a speaker 566 is coupled to the frame 564 andpositioned proximate an ear canal of the user 560. In some embodiments,another speaker, not shown, is positioned adjacent another ear canal ofthe user 560 to provide for stereo/shapeable sound control. The displaydevice 562 is operatively coupled, such as by a wired lead or wirelessconnectivity 568, to a local data processing module 570 which may bemounted in a variety of configurations, such as fixedly attached to theframe 564, fixedly attached to a helmet or hat worn by the user 560,embedded in headphones, or otherwise removably attached to the user 560(e.g., in a backpack-style configuration, in a belt-coupling styleconfiguration).

The local data processing module 570 may include a processor, as well asdigital memory, such as non-volatile memory (e.g., flash memory), bothof which may be utilized to assist in the processing, caching, andstorage of data. The data include data a) captured from sensors (whichmay be, e.g., operatively coupled to the frame 564) or otherwiseattached to the user 560, such as image capture devices (such ascameras), microphones, inertial measurement units, accelerometers,compasses, GPS units, radio devices, and/or gyros; and/or b) acquiredand/or processed using remote processing module 572 and/or remote datarepository 574, possibly for passage to the display device 562 aftersuch processing or retrieval.

In some embodiments, the wearable deice may communicate with remotecomponents. The local data processing module 570 may be operativelycoupled by communication links 576, 578, such as via a wired or wirelesscommunication links, to the remote processing module 572 and remote datarepository 574, respectively, such that these remote modules 572, 574are operatively coupled to each other and available as resources to thelocal data processing module 570. In further embodiments, in addition oras alternative to remote data repository 574, the wearable device canaccess cloud based remote data repositories, and/or services. In someembodiments, the headpose tracking component described above may be atleast partially implemented in the local data processing module 570. Insome embodiments, the world reconstruction component 516 in FIG. 3 maybe at least partially implemented in the local data processing module570. For example, the local data processing module 570 may be configuredto execute computer executable instructions to generate the map and/orthe physical world representations based at least in part on at least aportion of the data.

In some embodiments, processing may be distributed across local andremote processors. For example, local processing may be used toconstruct a map on a user device (e.g. tracking map) based on sensordata collected with sensors on that user's device. Such a map may beused by applications on that user's device. Additionally, previouslycreated maps (e.g., canonical maps) may be stored in remote datarepository 574. Where a suitable stored or persistent map is available,it may be used instead of or in addition to the tracking map createdlocally on the device. In some embodiments, a tracking map may belocalized to the stored map, such that a correspondence is establishedbetween a tracking map, which might be oriented relative to a positionof the wearable device at the time a user turned the system on, and thecanonical map, which may be oriented relative to one or more persistentfeatures. In some embodiments, the persistent map might be loaded on theuser device to allow the user device to render virtual content without adelay associated with scanning a location to build a tracking map of theuser's full environment from sensor data acquired during the scan. Insome embodiments, the user device may access a remote persistent map(e.g., stored on a cloud) without the need to download the persistentmap on the user device.

In some embodiments, spatial information may be communicated from thewearable device to remote services, such as a cloud service that isconfigured to localize a device to stored maps maintained on the cloudservice. According to one embodiment, the localization processing cantake place in the cloud matching the device location to existing maps,such as canonical maps, and return transforms that link virtual contentto the wearable device location. In such embodiments, the system canavoid communicating maps from remote resources to the wearable device.Other embodiments can be configured for both device-based andcloud-based localization, for example, to enable functionality wherenetwork connectivity is not available or a user opts not to enablecould-based localization.

Alternatively or additionally, the tracking map may be merged withpreviously stored maps to extend or improve the quality of those maps.The processing to determine whether a suitable previously createdenvironment map is available and/or to merge a tracking map with one ormore stored environment maps may be done in local data processing module570 or remote processing module 572.

In some embodiments, the local data processing module 570 may includeone or more processors (e.g., a graphics processing unit (GPU))configured to analyze and process data and/or image information. In someembodiments, the local data processing module 570 may include a singleprocessor (e.g., a single-core or multi-core ARM processor), which wouldlimit the local data processing module 570's compute budget but enable amore miniature device. In some embodiments, the world reconstructioncomponent 516 may use a compute budget less than a single Advanced RISCMachine (ARM) core to generate physical world representations inreal-time on a non-predefined space such that the remaining computebudget of the single ARM core can be accessed for other uses such as,for example, extracting meshes.

In some embodiments, the remote data repository 574 may include adigital data storage facility, which may be available through theInternet or other networking configuration in a “cloud” resourceconfiguration. In some embodiments, all data is stored and allcomputations are performed in the local data processing module 570,allowing fully autonomous use from a remote module. In some embodiments,all data is stored and all or most computations are performed in theremote data repository 574, allowing for a smaller device. A worldreconstruction, for example, may be stored in whole or in part in thisrepository 574.

In embodiments in which data is stored remotely, and accessible over anetwork, data may be shared by multiple users of an augmented realitysystem. For example, user devices may upload their tracking maps toaugment a database of environment maps. In some embodiments, thetracking map upload occurs at the end of a user session with a wearabledevice. In some embodiments, the tracking map uploads may occurcontinuously, semi-continuously, intermittently, at a pre-defined time,after a pre-defined period from the previous upload, or when triggeredby an event. A tracking map uploaded by any user device may be used toexpand or improve a previously stored map, whether based on data fromthat user device or any other user device. Likewise, a persistent mapdownloaded to a user device may be based on data from that user deviceor any other user device. In this way, high quality environment maps maybe readily available to users to improve their experiences with the ARsystem.

In further embodiments, persistent map downloads can be limited and/oravoided based on localization executed on remote resources (e.g., in thecloud). In such configurations, a wearable device or other XR devicecommunicates to the cloud service feature information coupled with poseinformation (e.g., positioning information for the device at the timethe features represented in the feature information were sensed). One ormore components of the cloud service may match the feature informationto respective stored maps (e.g., canonical maps) and generatestransforms between a tracking map maintained by the XR device and thecoordinate system of the canonical map. Each XR device that has itstracking map localized with respect to the canonical map may accuratelyrender virtual content in locations specified with respect to thecanonical map based on its own tracking.

In some embodiments, the local data processing module 570 is operativelycoupled to a battery 582. In some embodiments, the battery 582 is aremovable power source, such as over the counter batteries. In otherembodiments, the battery 582 is a lithium-ion battery. In someembodiments, the battery 582 includes both an internal lithium-ionbattery chargeable by the user 560 during non-operation times of thesystem 580 and removable batteries such that the user 560 may operatethe system 580 for longer periods of time without having to be tetheredto a power source to charge the lithium-ion battery or having to shutthe system 580 off to replace batteries.

FIG. 5A illustrates a user 530 wearing an AR display system rendering ARcontent as the user 530 moves through a physical world environment 532(hereinafter referred to as “environment 532”). The information capturedby the AR system along the movement path of the user may be processedinto one or more tracking maps. The user 530 positions the AR displaysystem at positions 534, and the AR display system records ambientinformation of a passable world (e.g., a digital representation of thereal objects in the physical world that can be stored and updated withchanges to the real objects in the physical world) relative to thepositions 534. That information may be stored as poses in combinationwith images, features, directional audio inputs, or other desired data.The positions 534 are aggregated to data inputs 536, for example, aspart of a tracking map, and processed at least by a passable worldmodule 538, which may be implemented, for example, by processing on aremote processing module 572 of FIG. 4 . In some embodiments, thepassable world module 538 may include the headpose component 514 and theworld reconstruction component 516, such that the processed informationmay indicate the location of objects in the physical world incombination with other information about physical objects used inrendering virtual content.

The passable world module 538 determines, at least in part, where andhow AR content 540 can be placed in the physical world as determinedfrom the data inputs 536. The AR content is “placed” in the physicalworld by presenting via the user interface both a representation of thephysical world and the AR content, with the AR content rendered as if itwere interacting with objects in the physical world and the objects inthe physical world presented as if the AR content were, whenappropriate, obscuring the user's view of those objects. In someembodiments, the AR content may be placed by appropriately selectingportions of a fixed element 542 (e.g., a table) from a reconstruction(e.g., the reconstruction 518) to determine the shape and position ofthe AR content 540. As an example, the fixed element may be a table andthe virtual content may be positioned such that it appears to be on thattable. In some embodiments, the AR content may be placed withinstructures in a field of view 544, which may be a present field of viewor an estimated future field of view. In some embodiments, the ARcontent may be persisted relative to a model 546 of the physical world(e.g. a mesh).

As depicted, the fixed element 542 serves as a proxy (e.g. digital copy)for any fixed element within the physical world which may be stored inthe passable world module 538 so that the user 530 can perceive contenton the fixed element 542 without the system having to map to the fixedelement 542 each time the user 530 sees it. The fixed element 542 may,therefore, be a mesh model from a previous modeling session ordetermined from a separate user but nonetheless stored by the passableworld module 538 for future reference by a plurality of users.Therefore, the passable world module 538 may recognize the environment532 from a previously mapped environment and display AR content withouta device of the user 530 mapping all or part of the environment 532first, saving computation process and cycles and avoiding latency of anyrendered AR content.

The mesh model 546 of the physical world may be created by the ARdisplay system and appropriate surfaces and metrics for interacting anddisplaying the AR content 540 can be stored by the passable world module538 for future retrieval by the user 530 or other users without the needto completely or partially recreate the model. In some embodiments, thedata inputs 536 are inputs such as geolocation, user identification, andcurrent activity to indicate to the passable world module 538 whichfixed element 542 of one or more fixed elements are available, which ARcontent 540 has last been placed on the fixed element 542, and whetherto display that same content (such AR content being “persistent” contentregardless of user viewing a particular passable world model).

Even in embodiments in which objects are considered to be fixed (e.g. akitchen table), the passable world module 538 may update those objectsin a model of the physical world from time to time to account for thepossibility of changes in the physical world. The model of fixed objectsmay be updated with a very low frequency. Other objects in the physicalworld may be moving or otherwise not regarded as fixed (e.g. kitchenchairs). To render an AR scene with a realistic feel, the AR system mayupdate the position of these non-fixed objects with a much higherfrequency than is used to update fixed objects. To enable accuratetracking of all of the objects in the physical world, an AR system maydraw information from multiple sensors, including one or more imagesensors.

FIG. 5B is a schematic illustration of a viewing optics assembly 548 andattendant components. In some embodiments, two eye tracking cameras 550,directed toward user eyes 549, detect metrics of the user eyes 549, suchas eye shape, eyelid occlusion, pupil direction and glint on the usereyes 549.

In some embodiments, one of the sensors may be a depth sensor 551, suchas a time of flight sensor, emitting signals to the world and detectingreflections of those signals from nearby objects to determine distanceto given objects. A depth sensor, for example, may quickly determinewhether objects have entered the field of view of the user, either as aresult of motion of those objects or a change of pose of the user.However, information about the position of objects in the field of viewof the user may alternatively or additionally be collected with othersensors. Depth information, for example, may be obtained fromstereoscopic visual image sensors or plenoptic sensors.

In some embodiments, world cameras 552 record a greater-than-peripheralview to map and/or otherwise create a model of the environment 532 anddetect inputs that may affect AR content. In some embodiments, the worldcamera 552 and/or camera 553 may be grayscale and/or color imagesensors, which may output grayscale and/or color image frames at fixedtime intervals. Camera 553 may further capture physical world imageswithin a field of view of the user at a specific time. Pixels of aframe-based image sensor may be sampled repetitively even if theirvalues are unchanged. Each of the world cameras 552, the camera 553 andthe depth sensor 551 have respective fields of view of 554, 555, and 556to collect data from and record a physical world scene, such as thephysical world environment 532 depicted in FIG. 34A.

Inertial measurement units 557 may determine movement and orientation ofthe viewing optics assembly 548. In some embodiments, inertialmeasurement units 557 may provide an output indicating a direction ofgravity. In some embodiments, each component is operatively coupled toat least one other component. For example, the depth sensor 551 isoperatively coupled to the eye tracking cameras 550 as a confirmation ofmeasured accommodation against actual distance the user eyes 549 arelooking at.

It should be appreciated that a viewing optics assembly 548 may includesome of the components illustrated in FIG. 34B and may includecomponents instead of or in addition to the components illustrated. Insome embodiments, for example, a viewing optics assembly 548 may includetwo world camera 552 instead of four. Alternatively or additionally,cameras 552 and 553 need not capture a visible light image of their fullfield of view. A viewing optics assembly 548 may include other types ofcomponents. In some embodiments, a viewing optics assembly 548 mayinclude one or more dynamic vision sensor (DVS), whose pixels mayrespond asynchronously to relative changes in light intensity exceedinga threshold.

In some embodiments, a viewing optics assembly 548 may not include thedepth sensor 551 based on time of flight information. In someembodiments, for example, a viewing optics assembly 548 may include oneor more plenoptic cameras, whose pixels may capture light intensity andan angle of the incoming light, from which depth information can bedetermined. For example, a plenoptic camera may include an image sensoroverlaid with a transmissive diffraction mask (TDM). Alternatively oradditionally, a plenoptic camera may include an image sensor containingangle-sensitive pixels and/or phase-detection auto-focus pixels (PDAF)and/or micro-lens array (MLA). Such a sensor may serve as a source ofdepth information instead of or in addition to depth sensor 551.

It also should be appreciated that the configuration of the componentsin FIG. 5B is provided as an example. A viewing optics assembly 548 mayinclude components with any suitable configuration, which may be set toprovide the user with the largest field of view practical for aparticular set of components. For example, if a viewing optics assembly548 has one world camera 552, the world camera may be placed in a centerregion of the viewing optics assembly instead of at a side.

Information from the sensors in viewing optics assembly 548 may becoupled to one or more of processors in the system. The processors maygenerate data that may be rendered so as to cause the user to perceivevirtual content interacting with objects in the physical world. Thatrendering may be implemented in any suitable way, including generatingimage data that depicts both physical and virtual objects. In otherembodiments, physical and virtual content may be depicted in one sceneby modulating the opacity of a display device that a user looks throughat the physical world. The opacity may be controlled so as to create theappearance of the virtual object and also to block the user from seeingobjects in the physical world that are occluded by the virtual objects.In some embodiments, the image data may only include virtual contentthat may be modified such that the virtual content is perceived by auser as realistically interacting with the physical world (e.g. clipcontent to account for occlusions), when viewed through the userinterface.

The location on the viewing optics assembly 548 at which content isdisplayed to create the impression of an object at a particular locationmay depend on the physics of the viewing optics assembly. Additionally,the pose of the user's head with respect to the physical world and thedirection in which the user's eyes are looking may impact where in thephysical world content displayed at a particular location on the viewingoptics assembly content will appear. Sensors as described above maycollect this information, and or supply information from which thisinformation may be calculated, such that a processor receiving sensorinputs may compute where objects should be rendered on the viewingoptics assembly 548 to create a desired appearance for the user.

Regardless of how content is presented to a user, a model of thephysical world may be used so that characteristics of the virtualobjects, which can be impacted by physical objects, including the shape,position, motion, and visibility of the virtual object, can be correctlycomputed. In some embodiments, the model may include the reconstructionof a physical world, for example, the reconstruction 518.

That model may be created from data collected from sensors on a wearabledevice of the user. Though, in some embodiments, the model may becreated from data collected by multiple users, which may be aggregatedin a computing device remote from all of the users (and which may be “inthe cloud”).

The model may be created, at least in part, by a world reconstructionsystem such as, for example, the world reconstruction component 516 ofFIG. 3 depicted in more detail in FIG. 6A. The world reconstructioncomponent 516 may include a perception module 660 that may generate,update, and store representations for a portion of the physical world.In some embodiments, the perception module 660 may represent the portionof the physical world within a reconstruction range of the sensors asmultiple voxels. Each voxel may correspond to a 3D cube of apredetermined volume in the physical world, and include surfaceinformation, indicating whether there is a surface in the volumerepresented by the voxel. Voxels may be assigned values indicatingwhether their corresponding volumes have been determined to includesurfaces of physical objects, determined to be empty or have not yetbeen measured with a sensor and so their value is unknown. It should beappreciated that values indicating that voxels that are determined to beempty or unknown need not be explicitly stored, as the values of voxelsmay be stored in computer memory in any suitable way, including storingno information for voxels that are determined to be empty or unknown.

In addition to generating information for a persisted worldrepresentation, the perception module 660 may identify and outputindications of changes in a region around a user of an AR system.Indications of such changes may trigger updates to volumetric datastored as part of the persisted world, or trigger other functions, suchas triggering components 604 that generate AR content to update the ARcontent.

In some embodiments, the perception module 660 may identify changesbased on a signed distance function (SDF) model. The perception module660 may be configured to receive sensor data such as, for example, depthmaps 660 a and headposes 660 b, and then fuse the sensor data into a SDFmodel 660 c. Depth maps 660 a may provide SDF information directly, andimages may be processed to arrive at SDF information. The SDFinformation represents distance from the sensors used to capture thatinformation. As those sensors may be part of a wearable unit, the SDFinformation may represent the physical world from the perspective of thewearable unit and therefore the perspective of the user. The headposes660 b may enable the SDF information to be related to a voxel in thephysical world.

In some embodiments, the perception module 660 may generate, update, andstore representations for the portion of the physical world that iswithin a perception range. The perception range may be determined based,at least in part, on a sensor's reconstruction range, which may bedetermined based, at least in part, on the limits of a sensor'sobservation range. As a specific example, an active depth sensor thatoperates using active IR pulses may operate reliably over a range ofdistances, creating the observation range of the sensor, which may befrom a few centimeters or tens of centimeters to a few meters.

The world reconstruction component 516 may include additional modulesthat may interact with the perception module 660. In some embodiments, apersisted world module 662 may receive representations for the physicalworld based on data acquired by the perception module 660. The persistedworld module 662 also may include various formats of representations ofthe physical world. For example, volumetric metadata 662 b such asvoxels may be stored as well as meshes 662 c and planes 662 d. In someembodiments, other information, such as depth maps could be saved.

In some embodiments, representations of the physical world, such asthose illustrated in FIG. 6A may provide relatively dense informationabout the physical world in comparison to sparse maps, such as atracking map based on feature points as described above.

In some embodiments, the perception module 660 may include modules thatgenerate representations for the physical world in various formatsincluding, for example, meshes 660 d, planes and semantics 660 e. Therepresentations for the physical world may be stored across local andremote storage mediums. The representations for the physical world maybe described in different coordinate frames depending on, for example,the location of the storage medium. For example, a representation forthe physical world stored in the device may be described in a coordinateframe local to the device. The representation for the physical world mayhave a counterpart stored in a cloud. The counterpart in the cloud maybe described in a coordinate frame shared by all devices in an XRsystem.

In some embodiments, these modules may generate representations based ondata within the perception range of one or more sensors at the time therepresentation is generated as well as data captured at prior times andinformation in the persisted world module 662. In some embodiments,these components may operate on depth information captured with a depthsensor. However, the AR system may include vision sensors and maygenerate such representations by analyzing monocular or binocular visioninformation.

In some embodiments, these modules may operate on regions of thephysical world. Those modules may be triggered to update a subregion ofthe physical world, when the perception module 660 detects a change inthe physical world in that subregion. Such a change, for example, may bedetected by detecting a new surface in the SDF model 660 c or othercriteria, such as changing the value of a sufficient number of voxelsrepresenting the subregion.

The world reconstruction component 516 may include components 664 thatmay receive representations of the physical world from the perceptionmodule 660. Information about the physical world may be pulled by thesecomponents according to, for example, a use request from an application.In some embodiments, information may be pushed to the use components,such as via an indication of a change in a pre-identified region or achange of the physical world representation within the perception range.The components 664, may include, for example, game programs and othercomponents that perform processing for visual occlusion, physics-basedinteractions, and environment reasoning.

Responding to the queries from the components 664, the perception module660 may send representations for the physical world in one or moreformats. For example, when the component 664 indicates that the use isfor visual occlusion or physics-based interactions, the perceptionmodule 660 may send a representation of surfaces. When the component 664indicates that the use is for environmental reasoning, the perceptionmodule 660 may send meshes, planes and semantics of the physical world.

In some embodiments, the perception module 660 may include componentsthat format information to provide the component 664. An example of sucha component may be raycasting component 660 f. A use component (e.g.,component 664), for example, may query for information about thephysical world from a particular point of view. Raycasting component 660f may select from one or more representations of the physical world datawithin a field of view from that point of view.

As should be appreciated from the foregoing description, the perceptionmodule 660, or another component of an AR system, may process data tocreate 3D representations of portions of the physical world. Data to beprocessed may be reduced by culling parts of a 3D reconstruction volumebased at last in part on a camera frustum and/or depth image, extractingand persisting plane data, capturing, persisting, and updating 3Dreconstruction data in blocks that allow local update while maintainingneighbor consistency, providing occlusion data to applicationsgenerating such scenes, where the occlusion data is derived from acombination of one or more depth data sources, and/or performing amulti-stage mesh simplification. The reconstruction may contain data ofdifferent levels of sophistication including, for example, raw data suchas live depth data, fused volumetric data such as voxels, and computeddata such as meshes.

In some embodiments, components of a passable world model may bedistributed, with some portions executing locally on an XR device andsome portions executing remotely, such as on a network connected server,or otherwise in the cloud. The allocation of the processing and storageof information between the local XR device and the cloud may impactfunctionality and user experience of an XR system. For example, reducingprocessing on a local device by allocating processing to the cloud mayenable longer battery life and reduce heat generated on the localdevice. But, allocating too much processing to the cloud may createundesirable latency that causes an unacceptable user experience.

FIG. 6B depicts a distributed component architecture 600 configured forspatial computing, according to some embodiments. The distributedcomponent architecture 600 may include a passable world component 602(e.g., PW 538 in FIG. 5A), a Lumin OS 604, API's 606, SDK 608, andApplication 610. The Lumin OS 604 may include a Linux-based kernel withcustom drivers compatible with an XR device. The API's 606 may includeapplication programming interfaces that grant XR applications (e.g.,Applications 610) access to the spatial computing features of an XRdevice. The SDK 608 may include a software development kit that allowsthe creation of XR applications.

One or more components in the architecture 600 may create and maintain amodel of a passable world. In this example sensor data is collected on alocal device. Processing of that sensor data may be performed in partlocally on the XR device and partially in the cloud. PW 538 may includeenvironment maps created based, at least in part, on data captured by ARdevices worn by multiple users. During sessions of an AR experience,individual AR devices (such as wearable devices described above inconnection with FIG. 4 may create tracking maps, which is one type ofmap.

In some embodiments, the device may include components that constructboth sparse maps and dense maps. A tracking map may serve as a sparsemap and may include headposes of the AR device scanning an environmentas well as information about objects detected within that environment ateach headpose. Those headposes may be maintained locally for eachdevice. For example, the headpose on each device may be relative to aninitial headpose when the device was turned on for its session. As aresult, each tracking map may be local to the device creating it and mayhave its own frame of reference defined by its own local coordinatesystem. In some embodiments, however, the tracking map on each devicemay be formed such that one coordinate of its local coordinate system isaligned with the direction of gravity as measured by its sensors, suchas inertial measurement unit 557.

The dense map may include surface information, which may be representedby a mesh or depth information. Alternatively or additionally, a densemap may include higher level information derived from surface or depthinformation, such as the location and/or characteristics of planesand/or other objects.

Creation of the dense maps may be independent of the creation of sparsemaps, in some embodiments. The creation of dense maps and sparse maps,for example, may be performed in separate processing pipelines within anAR system. Separating processing, for example, may enable generation orprocessing of different types of maps to be performed at differentrates. Sparse maps, for example, may be refreshed at a faster rate thandense maps. In some embodiments, however, the processing of dense andsparse maps may be related, even if performed in different pipelines.Changes in the physical world revealed in a sparse map, for example, maytrigger updates of a dense map, or vice versa. Further, even ifindependently created, the maps might be used together. For example, acoordinate system derived from a sparse map may be used to defineposition and/or orientation of objects in a dense map.

The sparse map and/or dense map may be persisted for re-use by the samedevice and/or sharing with other devices. Such persistence may beachieved by storing information in the cloud. The AR device may send thetracking map to a cloud to, for example, merge with environment mapsselected from persisted maps previously stored in the cloud. In someembodiments, the selected persisted maps may be sent from the cloud tothe AR device for merging. In some embodiments, the persisted maps maybe oriented with respect to one or more persistent coordinate frames.Such maps may serve as canonical maps, as they can be used by any ofmultiple devices. In some embodiments, a model of a passable world maycomprise or be created from one or more canonical maps. Devices, eventhough they perform some operations based on a coordinate frame local tothe device, may nonetheless use the canonical map by determining atransformation between their coordinate frame local to the device andthe canonical map.

A canonical map may originate as a tracking map (TM) (e.g., TM 1102 inFIG. 31A), which may be promoted to a canonical map. The canonical mapmay be persisted such that devices that access the canonical map may,once determining a transformation between their local coordinate systemand a coordinate system of the canonical map, use the information in thecanonical map to determine locations of objects represented in thecanonical map in the physical world around the device. In someembodiments, a TM may be a headpose sparse map created by an XR device.In some embodiments, the canonical map may be created when an XR devicesends one or more TMs to a cloud server for merging with additional TMscaptured by the XR device at a different time or by other XR devices.

In embodiments in which tracking maps are formed on local devices withone coordinate of a local coordinate frame aligned with gravity, thisorientation with respect to gravity may be preserved upon creation of acanonical map. For example, when a tracking map that is submitted formerging does not overlap with any previously stored map, that trackingmap may be promoted to a canonical map. Other tracking maps, which mayalso have an orientation relative to gravity, may be subsequently mergedwith that canonical map. The merging may be done so as to ensure thatthe resulting canonical map retains its orientation relative to gravity.Two maps, for example, may not be merged, regardless of correspondenceof feature points in those maps, if coordinates of each map aligned withgravity do not align with each other with a sufficiently closetolerance.

The canonical maps, or other maps, may provide information about theportions of the physical world represented by the data processed tocreate respective maps. FIG. 7 depicts an exemplary tracking map 700,according to some embodiments. The tracking map 700 may provide a floorplan 706 of physical objects in a corresponding physical world,represented by points 702. In some embodiments, a map point 702 mayrepresent a feature of a physical object that may include multiplefeatures. For example, each corner of a table may be a feature that isrepresented by a point on a map. The features may be derived fromprocessing images, such as may be acquired with the sensors of awearable device in an augmented reality system. The features, forexample, may be derived by processing an image frame output by a sensorto identify features based on large gradients in the image or othersuitable criteria. Further processing may limit the number of featuresin each frame. For example, processing may select features that likelyrepresent persistent objects. One or more heuristics may be applied forthis selection.

The tracking map 700 may include data on points 702 collected by adevice. For each image frame with data points included in a trackingmap, a pose may be stored. The pose may represent the orientation fromwhich the image frame was captured, such that the feature points withineach image frame may be spatially correlated. The pose may be determinedby positioning information, such as may be derived from the sensors,such as an IMU sensor, on the wearable device. Alternatively oradditionally, the pose may be determined from matching image frames toother image frames that depict overlapping portions of the physicalworld. By finding such positional correlation, which may be accomplishedby matching subsets of features points in two frames, the relative posebetween the two frames may be computed. A relative pose may be adequatefor a tracking map, as the map may be relative to a coordinate systemlocal to a device established based on the initial pose of the devicewhen construction of the tracking map was initiated.

Not all of the feature points and image frames collected by a device maybe retained as part of the tracking map, as much of the informationcollected with the sensors is likely to be redundant. Rather, onlycertain frames may be added to the map. Those frames may be selectedbased on one or more criteria, such as degree of overlap with imageframes already in the map, the number of new features they contain or aquality metric for the features in the frame. Image frames not added tothe tracking map may be discarded or may be used to revise the locationof features. As a further alternative, all or most of the image frames,represented as a set of features may be retained, but a subset of thoseframes may be designated as key frames, which are used for furtherprocessing.

The key frames may be processed to produce keyrigs 704. The key framesmay be processed to produce three dimensional sets of feature points andsaved as keyrigs 704. Such processing may entail, for example, comparingimage frames derived simultaneously from two cameras to stereoscopicallydetermine the 3D position of feature points. Metadata may be associatedwith these keyframes and/or keyrigs, such as poses.

The environment maps may have any of multiple formats depending on, forexample, the storage locations of an environment map including, forexample, local storage of AR devices and remote storage. For example, amap in remote storage may have higher resolution than a map in localstorage on a wearable device where memory is limited. To send a higherresolution map from remote storage to local storage, the map may be downsampled or otherwise converted to an appropriate format, such as byreducing the number of poses per area of the physical world stored inthe map and/or the number of feature points stored for each pose. Insome embodiments, a slice or portion of a high resolution map fromremote storage may be sent to local storage, where the slice or portionis not down sampled.

A database of environment maps may be updated as new tracking maps arecreated. To determine which of a potentially very large number ofenvironment maps in a database is to be updated, updating may includeefficiently selecting one or more environment maps stored in thedatabase relevant to the new tracking map. The selected one or moreenvironment maps may be ranked by relevance and one or more of thehighest ranking maps may be selected for processing to merge higherranked selected environment maps with the new tracking map to create oneor more updated environment maps. When a new tracking map represents aportion of the physical world for which there is no preexistingenvironment map to update, that tracking map may be stored in thedatabase as a new environment map.

View Independent Display

Described herein are methods and apparatus for providing virtualcontents using an XR system, independent of locations of eyes viewingthe virtual content. Conventionally, a virtual content is re-renderedupon any motion of the displaying system. For example, if a user wearinga display system views a virtual representation of a three-dimensional(3D) object on the display and walks around the area where the 3D objectappears, the 3D object should be re-rendered for each viewpoint suchthat the user has the perception that he or she is walking around anobject that occupies real space. However, the re-rendering consumessignificant computational resources of a system and causes artifacts dueto latency.

The inventors have recognized and appreciated that headpose (e.g., thelocation and orientation of a user wearing an XR system) may be used torender a virtual content independent of eye rotations within a head ofthe user. In some embodiments, dynamic maps of a scene may be generatedbased on multiple coordinate frames in real space across one or moresessions such that virtual contents interacting with the dynamic mapsmay be rendered robustly, independent of eye rotations within the headof the user and/or independent of sensor deformations caused by, forexample, heat generated during high-speed, computation-intensiveoperation. In some embodiments, the configuration of multiple coordinateframes may enable a first XR device worn by a first user and a second XRdevice worn by a second user to recognize a common location in a scene.In some embodiments, the configuration of multiple coordinate frames mayenable users wearing XR devices to view a virtual content in a samelocation of a scene.

In some embodiments, a tracking map may be built in a world coordinateframe, which may have a world origin. The world origin may be the firstpose of an XR device when the XR device is powered on. The world originmay be aligned to gravity such that a developer of an XR application canget gravity alignment without extra work. Different tracking maps may bebuilt in different world coordinate frames because the tracking maps maybe captured by a same XR device at different sessions and/or differentXR devices worn by different users. In some embodiments, a session of anXR device may span from powering on to powering off the device. In someembodiments, an XR device may have a head coordinate frame, which mayhave a head origin. The head origin may be the current pose of an XRdevice when an image is taken. The difference between headpose of aworld coordinate frame and of a head coordinate frame may be used toestimate a tracking route.

In some embodiments, an XR device may have a camera coordinate frame,which may have a camera origin. The camera origin may be the currentpose of one or more sensors of an XR device. The inventors haverecognized and appreciated that the configuration of a camera coordinateframe enables robust displaying virtual contents independent of eyerotation within a head of a user. This configuration also enables robustdisplaying of virtual contents independent of sensor deformation due to,for example, heat generated during operation.

In some embodiments, an XR device may have a head unit with ahead-mountable frame that a user can secure to their head and mayinclude two waveguides, one in front of each eye of the user. Thewaveguides may be transparent so that ambient light from real-worldobjects can transmit through the waveguides and the user can see thereal-world objects. Each waveguide may transmit projected light from aprojector to a respective eye of the user. The projected light may forman image on the retina of the eye. The retina of the eye thus receivesthe ambient light and the projected light. The user may simultaneouslysee real-world objects and one or more virtual objects that are createdby the projected light. In some embodiments, XR devices may have sensorsthat detect real-world objects around a user. These sensors may, forexample, be cameras that capture images that may be processed toidentify the locations of real-world objects.

In some embodiments, an XR system may assign a coordinate frame to avirtual content, as opposed to attaching the virtual content in a worldcoordinate frame. Such configuration enables a virtual content to bedescribed without regard to where it is rendered for a user, but it maybe attached to a more persistent frame position such as a persistentcoordinate frame (PCF) described in relation to, for example, FIGS.14-20C, to be rendered in a specified location. When the locations ofthe objects change, the XR device may detect the changes in theenvironment map and determine movement of the head unit worn by the userrelative to real-world objects.

FIG. 8 illustrates a user experiencing virtual content, as rendered byan XR system 10, in a physical environment, according to someembodiments. The XR system may include a first XR device 12.1 that isworn by a first user 14.1, a network 18 and a server 20. The user 14.1is in a physical environment with a real object in the form of a table16.

In the illustrated example, the first XR device 12.1 includes a headunit 22, a belt pack 24 and a cable connection 26. The first user 14.1secures the head unit 22 to their head and the belt pack 24 remotelyfrom the head unit 22 on their waist. The cable connection 26 connectsthe head unit 22 to the belt pack 24. The head unit 22 includestechnologies that are used to display a virtual object or objects to thefirst user 14.1 while the first user 14.1 is permitted to see realobjects such as the table 16. The belt pack 24 includes primarilyprocessing and communications capabilities of the first XR device 12.1.In some embodiments, the processing and communication capabilities mayreside entirely or partially in the head unit 22 such that the belt pack24 may be removed or may be located in another device such as abackpack.

In the illustrated example, the belt pack 24 is connected via a wirelessconnection to the network 18. The server 20 is connected to the network18 and holds data representative of local content. The belt pack 24downloads the data representing the local content from the server 20 viathe network 18. The belt pack 24 provides the data via the cableconnection 26 to the head unit 22. The head unit 22 may include adisplay that has a light source, for example, a laser light source or alight emitting diode (LED), and a waveguide that guides the light.

In some embodiments, the first user 14.1 may mount the head unit 22 totheir head and the belt pack 24 to their waist. The belt pack 24 maydownload image data representing virtual content over the network 18from the server 20. The first user 14.1 may see the table 16 through adisplay of the head unit 22. A projector forming part of the head unit22 may receive the image data from the belt pack 24 and generate lightbased on the image data. The light may travel through one or more of thewaveguides forming part of the display of the head unit 22. The lightmay then leave the waveguide and propagates onto a retina of an eye ofthe first user 14.1. The projector may generate the light in a patternthat is replicated on a retina of the eye of the first user 14.1. Thelight that falls on the retina of the eye of the first user 14.1 mayhave a selected field of depth so that the first user 14.1 perceives animage at a preselected depth behind the waveguide. In addition, botheyes of the first user 14.1 may receive slightly different images sothat a brain of the first user 14.1 perceives a three-dimensional imageor images at selected distances from the head unit 22. In theillustrated example, the first user 14.1 perceives a virtual content 28above the table 16. The proportions of the virtual content 28 and itslocation and distance from the first user 14.1 are determined by thedata representing the virtual content 28 and various coordinate framesthat are used to display the virtual content 28 to the first user 14.1.

In the illustrated example, the virtual content 28 is not visible fromthe perspective of the drawing and is visible to the first user 14.1through using the first XR device 12.1. The virtual content 28 mayinitially reside as data structures within vision data and algorithms inthe belt pack 24. The data structures may then manifest themselves aslight when the projectors of the head unit 22 generate light based onthe data structures. It should be appreciated that although the virtualcontent 28 has no existence in three-dimensional space in front of thefirst user 14.1, the virtual content 28 is still represented in FIG. 1in three-dimensional space for illustration of what a wearer of headunit 22 perceives. The visualization of computer data inthree-dimensional space may be used in this description to illustratehow the data structures that facilitate the renderings are perceived byone or more users relate to one another within the data structures inthe belt pack 24.

FIG. 9 illustrates components of the first XR device 12.1, according tosome embodiments. The first XR device 12.1 may include the head unit 22,and various components forming part of the vision data and algorithmsincluding, for example, a rendering engine 30, various coordinatesystems 32, various origin and destination coordinate frames 34, andvarious origin to destination coordinate frame transformers 36. Thevarious coordinate systems may be based on intrinsics of to the XRdevice or may be determined by reference to other information, such as apersistent pose or a persistent coordinate system, as described herein.

The head unit 22 may include a head-mountable frame 40, a display system42, a real object detection camera 44, a movement tracking camera 46,and an inertial measurement unit 48.

The head-mountable frame 40 may have a shape that is securable to thehead of the first user 14.1 in FIG. 8 . The display system 42, realobject detection camera 44, movement tracking camera 46, and inertialmeasurement unit 48 may be mounted to the head-mountable frame 40 andtherefore move together with the head-mountable frame 40.

The coordinate systems 32 may include a local data system 52, a worldframe system 54, a head frame system 56, and a camera frame system 58.

The local data system 52 may include a data channel 62, a local framedetermining routine 64 and a local frame storing instruction 66. Thedata channel 62 may be an internal software routine, a hardwarecomponent such as an external cable or a radio frequency receiver, or ahybrid component such as a port that is opened up. The data channel 62may be configured to receive image data 68 representing a virtualcontent.

The local frame determining routine 64 may be connected to the datachannel 62. The local frame determining routine 64 may be configured todetermine a local coordinate frame 70. In some embodiments, the localframe determining routine may determine the local coordinate frame basedon real world objects or real world locations. In some embodiments, thelocal coordinate frame may be based on a top edge relative to a bottomedge of a browser window, head or feet of a character, a node on anouter surface of a prism or bounding box that encloses the virtualcontent, or any other suitable location to place a coordinate frame thatdefines a facing direction of a virtual content and a location (e.g. anode, such as a placement node or PCF node) with which to place thevirtual content, etc.

The local frame storing instruction 66 may be connected to the localframe determining routine 64. One skilled in the art will understandthat software modules and routines are “connected” to one anotherthrough subroutines, calls, etc. The local frame storing instruction 66may store the local coordinate frame 70 as a local coordinate frame 72within the origin and destination coordinate frames 34. In someembodiments, the origin and destination coordinate frames 34 may be oneor more coordinate frames that may be manipulated or transformed inorder for a virtual content to persist between sessions. In someembodiments, a session may be the period of time between a boot-up andshut-down of an XR device. Two sessions may be two start-up andshut-down periods for a single XR device, or may be a start-up andshut-down for two different XR devices.

In some embodiments, the origin and destination coordinate frames 34 maybe the coordinate frames involved in one or more transformationsrequired in order for a first user's XR device and a second user's XRdevice to recognize a common location. In some embodiments, thedestination coordinate frame may be the output of a series ofcomputations and transformations applied to the target coordinate framein order for a first and second user to view a virtual content in thesame location.

The rendering engine 30 may be connected to the data channel 62. Therendering engine 30 may receive the image data 68 from the data channel62 such that the rendering engine 30 may render virtual content based,at least in part, on the image data 68.

The display system 42 may be connected to the rendering engine 30. Thedisplay system 42 may include components that transform the image data68 into visible light. The visible light may form two patterns, one foreach eye. The visible light may enter eyes of the first user 14.1 inFIG. 8 and may be detected on retinas of the eyes of the first user14.1.

The real object detection camera 44 may include one or more cameras thatmay capture images from different sides of the head-mountable frame 40.The movement tracking camera 46 may include one or more cameras thatcapture images on sides of the head-mountable frame 40. One set of oneor more cameras may be used instead of the two sets of one or morecameras representing the real object detection camera(s) 44 and themovement tracking camera(s) 46. In some embodiments, the cameras 44, 46may capture images. As described above these cameras may collect datathat is used to construct a tacking map.

The inertial measurement unit 48 may include a number of devices thatare used to detect movement of the head unit 22. The inertialmeasurement unit 48 may include a gravitation sensor, one or moreaccelerometers and one or more gyroscopes. The sensors of the inertialmeasurement unit 48, in combination, track movement of the head unit 22in at least three orthogonal directions and about at least threeorthogonal axes.

In the illustrated example, the world frame system 54 includes a worldsurface determining routine 78, a world frame determining routine 80,and a world frame storing instruction 82. The world surface determiningroutine 78 is connected to the real object detection camera 44. Theworld surface determining routine 78 receives images and/or key framesbased on the images that are captured by the real object detectioncamera 44 and processes the images to identify surfaces in the images. Adepth sensor (not shown) may determine distances to the surfaces. Thesurfaces are thus represented by data in three dimensions includingtheir sizes, shapes, and distances from the real object detectioncamera.

In some embodiments, a world coordinate frame 84 may be based on theorigin at the initialization of the headpose session. In someembodiments, the world coordinate frame may be located where the devicewas booted up, or could be somewhere new if headpose was lost during theboot session. In some embodiments, the world coordinate frame may be theorigin at the start of a headpose session.

In the illustrated example, the world frame determining routine 80 isconnected to the world surface determining routine 78 and determines aworld coordinate frame 84 based on the locations of the surfaces asdetermined by the world surface determining routine 78. The world framestoring instruction 82 is connected to the world frame determiningroutine 80 to receive the world coordinate frame 84 from the world framedetermining routine 80. The world frame storing instruction 82 storesthe world coordinate frame 84 as a world coordinate frame 86 within theorigin and destination coordinate frames 34.

The head frame system 56 may include a head frame determining routine 90and a head frame storing instruction 92. The head frame determiningroutine 90 may be connected to the movement tracking camera 46 and theinertial measurement unit 48. The head frame determining routine 90 mayuse data from the movement tracking camera 46 and the inertialmeasurement unit 48 to calculate a head coordinate frame 94. Forexample, the inertial measurement unit 48 may have a gravitation sensorthat determines the direction of gravitational force relative to thehead unit 22. The movement tracking camera 46 may continually captureimages that are used by the head frame determining routine 90 to refinethe head coordinate frame 94. The head unit 22 moves when the first user14.1 in FIG. 8 moves their head. The movement tracking camera 46 and theinertial measurement unit 48 may continuously provide data to the headframe determining routine 90 so that the head frame determining routine90 can update the head coordinate frame 94.

The head frame storing instruction 92 may be connected to the head framedetermining routine 90 to receive the head coordinate frame 94 from thehead frame determining routine 90. The head frame storing instruction 92may store the head coordinate frame 94 as a head coordinate frame 96among the origin and destination coordinate frames 34. The head framestoring instruction 92 may repeatedly store the updated head coordinateframe 94 as the head coordinate frame 96 when the head frame determiningroutine 90 recalculates the head coordinate frame 94. In someembodiments, the head coordinate frame may be the location of thewearable XR device 12.1 relative to the local coordinate frame 72.

The camera frame system 58 may include camera intrinsics 98. The cameraintrinsics 98 may include dimensions of the head unit 22 that arefeatures of its design and manufacture. The camera intrinsics 98 may beused to calculate a camera coordinate frame 100 that is stored withinthe origin and destination coordinate frames 34.

In some embodiments, the camera coordinate frame 100 may include allpupil positions of a left eye of the first user 14.1 in FIG. 8 . Whenthe left eye moves from left to right or up and down, the pupilpositions of the left eye are located within the camera coordinate frame100. In addition, the pupil positions of a right eye are located withina camera coordinate frame 100 for the right eye. In some embodiments,the camera coordinate frame 100 may include the location of the camerarelative to the local coordinate frame when an image is taken.

The origin to destination coordinate frame transformers 36 may include alocal-to-world coordinate transformer 104, a world-to-head coordinatetransformer 106, and a head-to-camera coordinate transformer 108. Thelocal-to-world coordinate transformer 104 may receive the localcoordinate frame 72 and transform the local coordinate frame 72 to theworld coordinate frame 86. The transformation of the local coordinateframe 72 to the world coordinate frame 86 may be represented as a localcoordinate frame transformed to world coordinate frame 110 within theworld coordinate frame 86.

The world-to-head coordinate transformer 106 may transform from theworld coordinate frame 86 to the head coordinate frame 96. Theworld-to-head coordinate transformer 106 may transform the localcoordinate frame transformed to world coordinate frame 110 to the headcoordinate frame 96. The transformation may be represented as a localcoordinate frame transformed to head coordinate frame 112 within thehead coordinate frame 96.

The head-to-camera coordinate transformer 108 may transform from thehead coordinate frame 96 to the camera coordinate frame 100. Thehead-to-camera coordinate transformer 108 may transform the localcoordinate frame transformed to head coordinate frame 112 to a localcoordinate frame transformed to camera coordinate frame 114 within thecamera coordinate frame 100. The local coordinate frame transformed tocamera coordinate frame 114 may be entered into the rendering engine 30.The rendering engine 30 may render the image data 68 representing thelocal content 28 based on the local coordinate frame transformed tocamera coordinate frame 114.

FIG. 10 is a spatial representation of the various origin anddestination coordinate frames 34. The local coordinate frame 72, worldcoordinate frame 86, head coordinate frame 96, and camera coordinateframe 100 are represented in the figure. In some embodiments, the localcoordinate frame associated with the XR content 28 may have a positionand rotation (e.g. may provide a node and facing direction) relative toa local and/or world coordinate frame and/or PCF when the virtualcontent is placed in the real world so the virtual content may be viewedby the user. Each camera may have its own camera coordinate frame 100encompassing all pupil positions of one eye. Reference numerals 104A and106A represent the transformations that are made by the local-to-worldcoordinate transformer 104, world-to-head coordinate transformer 106,and head-to-camera coordinate transformer 108 in FIG. 9 , respectively.

FIG. 11 depicts a camera render protocol for transforming from a headcoordinate frame to a camera coordinate frame, according to someembodiments. In the illustrated example, a pupil for a single eye movesfrom position A to B. A virtual object that is meant to appearstationary will project onto a depth plane at one of the two positions Aor B depending on the position of the pupil (assuming that the camera isconfigured to use a pupil-based coordinate frame). As a result, using apupil coordinate frame transformed to a head coordinate frame will causejitter in a stationary virtual object as the eye moves from position Ato position B. This situation is referred to as view dependent displayor projection.

As depicted in FIG. 12 , a camera coordinate frame (e.g., CR) ispositioned and encompasses all pupil positions and object projectionwill now be consistent regardless of pupil positions A and B. The headcoordinate frame transforms to the CR frame, which is referred to asview independent display or projection. An image reprojection may beapplied to the virtual content to account for a change in eye position,however, as the rendering is still in the same position, jitter isminimized.

FIG. 13 illustrates the display system 42 in more detail. The displaysystem 42 includes a stereoscopic analyzer 144 that is connected to therendering engine 30 and forms part of the vision data and algorithms.

The display system 42 further includes left and right projectors 166Aand 166B and left and right waveguides 170A and 170B. The left and rightprojectors 166A and 166B are connected to power supplies. Each projector166A and 166B has a respective input for image data to be provided tothe respective projector 166A or 166B. The respective projector 166A or166B, when powered, generates light in two-dimensional patterns andemanates the light therefrom. The left and right waveguides 170A and170B are positioned to receive light from the left and right projectors166A and 166B, respectively. The left and right waveguides 170A and 170Bare transparent waveguides.

In use, a user mounts the head mountable frame 40 to their head.Components of the head mountable frame 40 may, for example, include astrap (not shown) that wraps around the back of the head of the user.The left and right waveguides 170A and 170B are then located in front ofleft and right eyes 220A and 220B of the user.

The rendering engine 30 enters the image data that it receives into thestereoscopic analyzer 144. The image data is three-dimensional imagedata of the local content 28 in FIG. 8 . The image data is projectedonto a plurality of virtual planes. The stereoscopic analyzer 144analyzes the image data to determine left and right image data setsbased on the image data for projection onto each depth plane. The leftand right image data sets are data sets that represent two-dimensionalimages that are projected in three-dimensions to give the user aperception of a depth.

The stereoscopic analyzer 144 enters the left and right image data setsinto the left and right projectors 166A and 166B. The left and rightprojectors 166A and 166B then create left and right light patterns. Thecomponents of the display system 42 are shown in plan view, although itshould be understood that the left and right patterns aretwo-dimensional patterns when shown in front elevation view. Each lightpattern includes a plurality of pixels. For purposes of illustration,light rays 224A and 226A from two of the pixels are shown leaving theleft projector 166A and entering the left waveguide 170A. The light rays224A and 226A reflect from sides of the left waveguide 170A. It is shownthat the light rays 224A and 226A propagate through internal reflectionfrom left to right within the left waveguide 170A, although it should beunderstood that the light rays 224A and 226A also propagate in adirection into the paper using refractory and reflective systems.

The light rays 224A and 226A exit the left light waveguide 170A througha pupil 228A and then enter a left eye 220A through a pupil 230A of theleft eye 220A. The light rays 224A and 226A then fall on a retina 232Aof the left eye 220A. In this manner, the left light pattern falls onthe retina 232A of the left eye 220A. The user is given the perceptionthat the pixels that are formed on the retina 232A are pixels 234A and236A that the user perceives to be at some distance on a side of theleft waveguide 170A opposing the left eye 220A. Depth perception iscreated by manipulating the focal length of the light.

In a similar manner, the stereoscopic analyzer 144 enters the rightimage data set into the right projector 166B. The right projector 166Btransmits the right light pattern, which is represented by pixels in theform of light rays 224B and 226B. The light rays 224B and 226B reflectwithin the right waveguide 170B and exit through a pupil 228B. The lightrays 224B and 226B then enter through a pupil 230B of the right eye 220Band fall on a retina 232B of a right eye 220B. The pixels of the lightrays 224B and 226B are perceived as pixels 134B and 236B behind theright waveguide 170B.

The patterns that are created on the retinas 232A and 232B areindividually perceived as left and right images. The left and rightimages differ slightly from one another due to the functioning of thestereoscopic analyzer 144. The left and right images are perceived in amind of the user as a three-dimensional rendering.

As mentioned, the left and right waveguides 170A and 170B aretransparent. Light from a real-life object such as the table 16 on aside of the left and right waveguides 170A and 170B opposing the eyes220A and 220B can project through the left and right waveguides 170A and170B and fall on the retinas 232A and 232B.

Persistent Coordinate Frame (PCF)

Described herein are methods and apparatus for providing spatialpersistence across user instances within a shared space. Without spatialpersistence, virtual content placed in the physical world by a user in asession may not exist or may be misplaced in the user's view in adifferent session. Without spatial persistence, virtual content placedin the physical world by one user may not exist or may be out of placein a second user's view, even if the second user is intended to besharing an experience of the same physical space with the first user.

The inventors have recognized and appreciated that spatial persistencemay be provided through persistent coordinate frames (PCFs). A PCF maybe defined based on one or more points, representing features recognizedin the physical world (e.g., corners, edges). The features may beselected such that they are likely to be the same from a user instanceto another user instance of an XR system.

Further, drift during tracking, which causes the computed tracking path(e.g., camera trajectory) to deviate from the actual tracking path, cancause the location of virtual content, when rendered with respect to alocal map that is based solely on a tracking map to appear out of place.A tracking map for the space may be refined to correct the drifts as anXR device collects more information of the scene overtime. However, ifvirtual content is placed on a real object before a map refinement andsaved with respect to the world coordinate frame of the device derivedfrom the tracking map, the virtual content may appear displaced, as ifthe real object has been moved during the map refinement. PCFs may beupdated according to map refinement because the PCFs are defined basedon the features and are updated as the features move during maprefinements.

A PCF may comprise six degrees of freedom with translations androtations relative to a map coordinate system. A PCF may be stored in alocal and/or remote storage medium. The translations and rotations of aPCF may be computed relative to a map coordinate system depending on,for example, the storage location. For example, a PCF used locally by adevice may have translations and rotations relative to a worldcoordinate frame of the device. A PCF in the cloud may have translationsand rotations relative to a canonical coordinate frame of a canonicalmap.

PCFs may provide a sparse representation of the physical world,providing less than all of the available information about the physicalworld, such that they may be efficiently processed and transferred.Techniques for processing persistent spatial information may includecreating dynamic maps based on one or more coordinate systems in realspace across one or more sessions, generating persistent coordinateframes (PCF) over the sparse maps, which may be exposed to XRapplications via, for example, an application programming interface(API).

FIG. 14 is a block diagram illustrating the creation of a persistentcoordinate frame (PCF) and the attachment of XR content to the PCF,according to some embodiments. Each block may represent digitalinformation stored in a computer memory. In the case of applications1180, the data may represent computer-executable instructions. In thecase of virtual content 1170, the digital information may define avirtual object, as specified by the application 1180, for example. Inthe case of the other boxes, the digital information may characterizesome aspect of the physical world.

In the illustrated embodiment, one or more PCFs are created from imagescaptured with sensors on a wearable device. In the embodiment of FIG. 14, the sensors are visual image cameras. These cameras may be the samecameras used for forming a tracking map. Accordingly, some of theprocessing suggested by FIG. 14 may be performed as part of updating atracking map. However, FIG. 14 illustrates that information thatprovides persistence is generated in addition to the tracking map.

In order to derive a 3D PCF, two images 1110 from two cameras mounted toa wearable device in a configuration that enables stereoscopic imageanalysis are processed together. FIG. 14 illustrates an Image 1 and anImage 2, each derived from one of the cameras. A single image from eachcamera is illustrated for simplicity. However, each camera may output astream of image frames and the processing illustrated in FIG. 14 may beperformed for multiple image frames in the stream.

Accordingly, Image 1 and Image 2 may each be one frame in a sequence ofimage frames. Processing as depicted in FIG. 14 may be repeated onsuccessive image frames in the sequence until image frames containingfeature points providing a suitable image from which to form persistentspatial information is processed. Alternatively or additionally, theprocessing of FIG. 14 might be repeated as a user moves such that theuser is no longer close enough to a previously identified PCF toreliably use that PCF for determining positions with respect to thephysical world. For example, an XR system may maintain a current PCF fora user. When that distance exceeds a threshold, the system may switch toa new current PCF, closer to the user, which may be generated accordingto the process of FIG. 14 , using image frames acquired in the user'scurrent location.

Even when generating a single PCF, a stream of image frames may beprocessed to identify image frames depicting content in the physicalworld that is likely stable and can be readily identified by a device inthe vicinity of the region of the physical world depicted in the imageframe. In the embodiment of FIG. 14 , this processing begins with theidentification of features 1120 in the image. Features may beidentified, for example, by finding locations of gradients in the imageabove a threshold or other characteristics, which may correspond to acorner of an object, for example. In the embodiment illustrated, thefeatures are points, but other recognizable features, such as edges, mayalternatively or additionally be used.

In the embodiment illustrated, a fixed number, N, of features 1120 areselected for further processing. Those feature points may be selectedbased on one or more criteria, such as magnitude of the gradient, orproximity to other feature points. Alternatively or additionally, thefeature points may be selected heuristically, such as based oncharacteristics that suggest the feature points are persistent. Forexample, heuristics may be defined based on the characteristics offeature points that likely correspond to a corner of a window or a dooror a large piece of furniture. Such heuristics may take into account thefeature point itself and what surrounds it. As a specific example, thenumber of feature points per image may be between 100 and 500 or between150 and 250, such as 200.

Regardless of the number of feature points selected, descriptors 1130may be computed for the feature points. In this example, a descriptor iscomputed for each selected feature point, but a descriptor may becomputed for groups of feature points or for a subset of the featurepoints or for all features within an image. The descriptor characterizesa feature point such that feature points representing the same object inthe physical world are assigned similar descriptors. The descriptors mayfacilitate alignment of two frames, such as may occur when one map islocalized with respect to another. Rather than searching for a relativeorientation of the frames that minimizes the distance between featurepoints of the two images, an initial alignment of the two frames may bemade by identifying feature points with similar descriptors. Alignmentof the image frames may be based on aligning points with similardescriptors, which may entail less processing than computing analignment of all the feature points in the images.

The descriptors may be computed as a mapping of the feature points or,in some embodiments a mapping of a patch of an image around a featurepoint, to a descriptor. The descriptor may be a numeric quantity. U.S.patent application Ser. No. 16/190,948 describes computing descriptorsfor feature points and is hereby incorporated herein by reference in itsentirety.

In the example of FIG. 14 , a descriptor 1130 is computed for eachfeature point in each image frame. Based on the descriptors and/or thefeature points and/or the image itself, the image frame may beidentified as a key frame 1140. In the embodiment illustrated, a keyframe is an image frame meeting certain criteria that is then selectedfor further processing. In making a tracking map, for example, imageframes that add meaningful information to the map may be selected as keyframes that are integrated into the map. On the other hand, image framesthat substantially overlap a region for which an image frame has alreadybeen integrated into the map may be discarded such that they do notbecome key frames. Alternatively or additionally, key frames may beselected based on the number and/or type of feature points in the imageframe. In the embodiment of FIG. 14 , key frames 1150 selected forinclusion in a tracking map may also be treated as key frames fordetermining a PCF, but different or additional criteria for selectingkey frames for generation of a PCF may be used.

Though FIG. 14 shows that a key frame is used for further processing,information acquired from an image may be processed in other forms. Forexample, the feature points, such as in a key rig, may alternatively oradditionally be processed. Moreover, though a key frame is described asbeing derived from a single image frame, it is not necessary that therebe a one to one relationship between a key frame and an acquired imageframe. A key frame, for example, may be acquired from multiple imageframes, such as by stitching together or aggregating the image framessuch that only features appearing in multiple images are retained in thekey frame.

A key frame may include image information and/or metadata associatedwith the image information. In some embodiments, images captured by thecameras 44, 46 (FIG. 9 ) may be computed into one or more key frames(e.g., key frames 1, 2). In some embodiments, a key frame may include acamera pose. In some embodiments, a key frame may include one or morecamera images captured at the camera pose. In some embodiments, an XRsystem may determine a portion of the camera images captured at thecamera pose as not useful and thus not include the portion in a keyframe. Therefore, using key frames to align new images with earlierknowledge of a scene reduces the use of computational resource of the XRsystem. In some embodiments, a key frame may include an image, and/orimage data, at a location with a direction/angle. In some embodiments, akey frame may include a location and a direction from which one or moremap points may be observed. In some embodiments, a key frame may includea coordinate frame with an ID. U.S. patent application Ser. No.15/877,359 describes key frames and is hereby incorporated herein byreference in its entirety.

Some or all of the key frames 1140 may be selected for furtherprocessing, such as the generation of a persistent pose 1150 for the keyframe. The selection may be based on the characteristics of all, or asubset of, the feature points in the image frame. Those characteristicsmay be determined from processing the descriptors, features and/or imageframe, itself. As a specific example, the selection may be based on acluster of feature points identified as likely to relate to a persistentobject.

Each key frame is associated with a pose of the camera at which that keyframe was acquired. For key frames selected for processing into apersistent pose, that pose information may be saved along with othermetadata about the key frame, such as a WiFi fingerprint and/or GPScoordinates at the time of acquisition and/or at the location ofacquisition.

The persistent poses are a source of information that a device may useto orient itself relative to previously acquired information about thephysical world. For example, if the key frame from which a persistentpose was created is incorporated into a map of the physical world, adevice may orient itself relative to that persistent pose using asufficient number of feature points in the key frame that are associatedwith the persistent pose. The device may align a current image that ittakes of its surroundings to the persistent pose. This alignment may bebased on matching the current image to the image 1110, the features1120, and/or the descriptors 1130 that gave rise to the persistent pose,or any subset of that image or those features or descriptors. In someembodiments, the current image frame that is matched to the persistentpose may be another key frame that has been incorporated into thedevice's tracking map.

Information about a persistent pose may be stored in a format thatfacilitates sharing among multiple applications, which may be executingon the same or different devices. In the example of FIG. 14 , some orall of the persistent poses may be reflected as a persistent coordinateframes (PCF) 1160. Like a persistent pose, a PCF may be associated witha map and may comprise a set of features, or other information, that adevice can use to determine its orientation with respect to that PCF.The PCF may include a transformation that defines its transformationwith respect to the origin of its map, such that, by correlating itsposition to a PCF, the device can determine its position with respect toany objects in the physical world reflected in the map.

As the PCF provides a mechanism for determining locations with respectto the physical objects, an application, such as applications 1180, maydefine positions of virtual objects with respect to one or more PCFs,which serve as anchors for the virtual content 1170. FIG. 14illustrates, for example, that App 1 has associated its virtual content2 with PCF 1.2. Likewise, App 2 has associated its virtual content 3with PCF 1.2. App 1 is also shown associating its virtual content 1 toPCF 4.5, and App 2 is shown associating its virtual content 4 with PCF3. In some embodiments, PCF 3 may be based on Image 3 (not shown), andPCF 4.5 may be based on Image 4 and Image 5 (not shown) analogously tohow PCF 1.2 is based on Image 1 and Image 2. When rendering this virtualcontent, a device may apply one or more transformations to computeinformation, such as the location of the virtual content with respect tothe display of the device and/or the location of physical objects withrespect to the desired location of the virtual content. Using the PCFsas reference may simplify such computations.

In some embodiments, a persistent pose may be a coordinate locationand/or direction that has one or more associated key frames. In someembodiments, a persistent pose may be automatically created after theuser has traveled a certain distance, e.g., three meters. In someembodiments, the persistent poses may act as reference points duringlocalization. In some embodiments, the persistent poses may be stored ina passable world (e.g., the passable world module 538).

In some embodiments, a new PCF may be determined based on a pre-defineddistance allowed between adjacent PCFs. In some embodiments, one or morepersistent poses may be computed into a PCF when a user travels apre-determined distance, e.g. five meters. In some embodiments, PCFs maybe associated with one or more world coordinate frames and/or canonicalcoordinate frames, e.g., in the passable world. In some embodiments,PCFs may be stored in a local and/or remote database depending on, forexample, security settings.

FIG. 15 illustrates a method 4700 of establishing and using apersistence coordinate frame, according to some embodiments. The method4700 may start from capturing (Act 4702) images (e.g., Image 1 and Image2 in FIG. 14 ) about a scene using one or more sensors of an XR device.Multiple cameras may be used and one camera may generate multipleimages, for example, in a stream.

The method 4700 may include extracting (4704) interest points (e.g., mappoints 702 in FIG. 7 , features 1120 in FIG. 14 ) from the capturedimages, generating (Act 4706) descriptors (e.g., descriptors 1130 inFIG. 14 ) for the extracted interest points, and generating (Act 4708)key frames (e.g., key frames 1140) based on the descriptors. In someembodiments, the method may compare interest points in the key frames,and form pairs of key frames that share a predetermined amount ofinterest points. The method may reconstruct parts of the physical worldusing individual pairs of key frames. Mapped parts of the physical worldmay be saved as 3D features (e.g., keyrig 704 in FIG. 7 ). In someembodiments, a selected portion of the pairs of key frames may be usedto build 3D features. In some embodiments, results of the mapping may beselectively saved. Key frames not used for building 3D features may beassociated with the 3D features through poses, for example, representingdistances between key frames with a covariance matrix between poses ofkeyframes. In some embodiments, pairs of key frames may be selected tobuild the 3D features such that distances between each two of the build3D features are within a predetermined distance, which may be determinedto balance the amount of computation needed and the level of accuracy ofa resulting model. Such approaches enable providing a model of thephysical world with the amount of data that is suitable for efficientand accurate computation with an XR system. In some embodiments, acovariance matrix of two images may include covariances between poses ofthe two images (e.g., six degrees of freedom).

The method 4700 may include generating (Act 4710) persistent poses basedon the key frames. In some embodiments, the method may includegenerating the persistent poses based on the 3D features reconstructedfrom pairs of key frames. In some embodiments, a persistent pose may beattached to a 3D feature. In some embodiments, the persistent pose mayinclude a pose of a key frame used to construct the 3D feature. In someembodiments, the persistent pose may include an average pose of keyframes used to construct the 3D feature. In some embodiments, persistentposes may be generated such that distances between neighboringpersistent poses are within a predetermined value, for example, in therange of one meter to five meters, any value in between, or any othersuitable value. In some embodiments, the distances between neighboringpersistent poses may be represented by a covariance matrix of theneighboring persistent poses.

The method 4700 may include generating (Act 4712) PCFs based on thepersistent poses. In some embodiments, a PCF may be attached to a 3Dfeature. In some embodiments, a PCF may be associated with one or morepersistent poses. In some embodiments, a PCF may include a pose of oneof the associated persistent poses. In some embodiments, a PCF mayinclude an average pose of the poses of the associated persistent poses.In some embodiments, PCFs may be generated such that distances betweenneighboring PCFs are within a predetermined value, for example, in therange of three meters to ten meters, any value in between, or any othersuitable value. In some embodiments, the distances between neighboringPCFs may be represented by a covariance matrix of the neighboring PCFs.In some embodiments, PCFs may be exposed to XR applications via, forexample, an application programming interface (API) such that the XRapplications can access a model of the physical world through the PCFswithout accessing the model itself.

The method 4700 may include associating (Act 4714) image data of avirtual object to be displayed by the XR device to at least one of thePCFs. In some embodiments, the method may include computing translationsand orientations of the virtual object with respect to the associatedPCF. It should be appreciated that it is not necessary to associate avirtual object to a PCF generated by the device placing the virtualobject. For example, a device may retrieve saved PCFs in a canonical mapin a cloud and associate a virtual object to a retrieved PCF. It shouldbe appreciated that the virtual object may move with the associated PCFas the PCF is adjusted overtime.

FIG. 16 illustrates the first XR device 12.1 and vision data andalgorithms of a second XR device 12.2 and the server 20, according tosome embodiments. The components illustrated in FIG. 16 may operate toperform some or all of the operations associated with generating,updating, and/or using spatial information, such as persistent poses,persistent coordinate frames, tracking maps, or canonical maps, asdescribed herein. Although not illustrated, the first XR device 12.1 maybe configured the same as the second XR device 12.2. The server 20 mayhave a map storing routine 118, a canonical map 120, a map transmitter122, and a map merge algorithm 124.

The second XR device 12.2, which may be in the same scene as the firstXR device 12.1, may include a persistent coordinate frame (PCF)integration unit 1300, an application 1302 that generates the image data68 that may be used to render a virtual object, and a frame embeddinggenerator 308 (See FIG. 21 ). In some embodiments, a map download system126, PCF identification system 128, Map 2, localization module 130,canonical map incorporator 132, canonical map 133, and map publisher 136may be grouped into a passable world unit 1304. The PCF integration unit1300 may be connected to the passable world unit 1304 and othercomponents of the second XR device 12.2 to allow for the retrieval,generation, use, upload, and download of PCFs.

A map, comprising PCFs, may enable more persistence in a changing world.In some embodiments, localizing a tracking map including, for example,matching features for images, may include selecting features thatrepresent persistent content from the map constituted by PCFs, whichenables fast matching and/or localizing. For example, a world wherepeople move into and out of the scene and objects such as doors moverelative to the scene, requires less storage space and transmissionrates, and enables the use of individual PCFs and their relationshipsrelative to one another (e.g., integrated constellation of PCFs) to mapa scene.

In some embodiments, the PCF integration unit 1300 may include PCFs 1306that were previously stored in a data store on a storage unit of thesecond XR device 12.2, a PCF tracker 1308, a persistent pose acquirer1310, a PCF checker 1312, a PCF generation system 1314, a coordinateframe calculator 1316, a persistent pose calculator 1318, and threetransformers, including a tracking map and persistent pose transformer1320, a persistent pose and PCF transformer 1322, and a PCF and imagedata transformer 1324.

In some embodiments, the PCF tracker 1308 may have an on-prompt and anoff-prompt that are selectable by the application 1302. The application1302 may be executable by a processor of the second XR device 12.2 to,for example, display a virtual content. The application 1302 may have acall that switches the PCF tracker 1308 on via the on-prompt. The PCFtracker 1308 may generate PCFs when the PCF tracker 1308 is switched on.The application 1302 may have a subsequent call that can switch the PCFtracker 1308 off via the off-prompt. The PCF tracker 1308 terminates PCFgeneration when the PCF tracker 1308 is switched off.

In some embodiments, the server 20 may include a plurality of persistentposes 1332 and a plurality of PCFs 1330 that have previously been savedin association with a canonical map 120. The map transmitter 122 maytransmit the canonical map 120 together with the persistent poses 1332and/or the PCFs 1330 to the second XR device 12.2. The persistent poses1332 and PCFs 1330 may be stored in association with the canonical map133 on the second XR device 12.2. When Map 2 localizes to the canonicalmap 133, the persistent poses 1332 and the PCFs 1330 may be stored inassociation with Map 2.

In some embodiments, the persistent pose acquirer 1310 may acquire thepersistent poses for Map 2. The PCF checker 1312 may be connected to thepersistent pose acquirer 1310. The PCF checker 1312 may retrieve PCFsfrom the PCFs 1306 based on the persistent poses retrieved by thepersistent pose acquirer 1310. The PCFs retrieved by the PCF checker1312 may form an initial group of PCFs that are used for image displaybased on PCFs.

In some embodiments, the application 1302 may require additional PCFs tobe generated. For example, if a user moves to an area that has notpreviously been mapped, the application 1302 may switch the PCF tracker1308 on. The PCF generation system 1314 may be connected to the PCFtracker 1308 and begin to generate PCFs based on Map 2 as Map 2 beginsto expand. The PCFs generated by the PCF generation system 1314 may forma second group of PCFs that may be used for PCF-based image display.

The coordinate frame calculator 1316 may be connected to the PCF checker1312. After the PCF checker 1312 retrieved PCFs, the coordinate framecalculator 1316 may invoke the head coordinate frame 96 to determine aheadpose of the second XR device 12.2. The coordinate frame calculator1316 may also invoke the persistent pose calculator 1318. The persistentpose calculator 1318 may be directly or indirectly connected to theframe embedding generator 308. In some embodiments, an image/frame maybe designated a key frame after a threshold distance from the previouskey frame, e.g. 3 meters, is traveled. The persistent pose calculator1318 may generate a persistent pose based on a plurality, for examplethree, key frames. In some embodiments, the persistent pose may beessentially an average of the coordinate frames of the plurality of keyframes.

The tracking map and persistent pose transformer 1320 may be connectedto Map 2 and the persistent pose calculator 1318. The tracking map andpersistent pose transformer 1320 may transform Map 2 to the persistentpose to determine the persistent pose at an origin relative to Map 2.

The persistent pose and PCF transformer 1322 may be connected to thetracking map and persistent pose transformer 1320 and further to the PCFchecker 1312 and the PCF generation system 1314. The persistent pose andPCF transformer 1322 may transform the persistent pose (to which thetracking map has been transformed) to the PCFs from the PCF checker 1312and the PCF generation system 1314 to determine the PCFs relative to thepersistent pose.

The PCF and image data transformer 1324 may be connected to thepersistent pose and PCF transformer 1322 and to the data channel 62. ThePCF and image data transformer 1324 transforms the PCFs to the imagedata 68. The rendering engine 30 may be connected to the PCF and imagedata transformer 1324 to display the image data 68 to the user relativeto the PCFs.

The PCF integration unit 1300 may store the additional PCFs that aregenerated with the PCF generation system 1314 within the PCFs 1306. ThePCFs 1306 may be stored relative to persistent poses. The map publisher136 may retrieve the PCFs 1306 and the persistent poses associated withthe PCFs 1306 when the map publisher 136 transmits Map 2 to the server20, the map publisher 136 also transmits the PCFs and persistent posesassociated with Map 2 to the server 20. When the map storing routine 118of the server 20 stores Map 2, the map storing routine 118 may alsostore the persistent poses and PCFs generated by the second viewingdevice 12.2. The map merge algorithm 124 may create the canonical map120 with the persistent poses and PCFs of Map 2 associated with thecanonical map 120 and stored within the persistent poses 1332 and PCFs1330, respectively.

The first XR device 12.1 may include a PCF integration unit similar tothe PCF integration unit 1300 of the second XR device 12.2. When the maptransmitter 122 transmits the canonical map 120 to the first XR device12.1, the map transmitter 122 may transmit the persistent poses 1332 andPCFs 1330 associated with the canonical map 120 and originating from thesecond XR device 12.2. The first XR device 12.1 may store the PCFs andthe persistent poses within a data store on a storage device of thefirst XR device 12.1. The first XR device 12.1 may then make use of thepersistent poses and the PCFs originating from the second XR device 12.2for image display relative to the PCFs. Additionally or alternatively,the first XR device 12.1 may retrieve, generate, make use, upload, anddownload PCFs and persistent poses in a manner similar to the second XRdevice 12.2 as described above.

In the illustrated example, the first XR device 12.1 generates a localtracking map (referred to hereinafter as “Map 1”) and the map storingroutine 118 receives Map 1 from the first XR device 12.1. The mapstoring routine 118 then stores Map 1 on a storage device of the server20 as the canonical map 120.

The second XR device 12.2 includes a map download system 126, an anchoridentification system 128, a localization module 130, a canonical mapincorporator 132, a local content position system 134, and a mappublisher 136.

In use, the map transmitter 122 sends the canonical map 120 to thesecond XR device 12.2 and the map download system 126 downloads andstores the canonical map 120 as a canonical map 133 from the server 20.

The anchor identification system 128 is connected to the world surfacedetermining routine 78. The anchor identification system 128 identifiesanchors based on objects detected by the world surface determiningroutine 78. The anchor identification system 128 generates a second map(Map 2) using the anchors. As indicated by the cycle 138, the anchoridentification system 128 continues to identify anchors and continues toupdate Map 2. The locations of the anchors are recorded asthree-dimensional data based on data provided by the world surfacedetermining routing 78. The world surface determining routine 78receives images from the real object detection camera 44 and depth datafrom depth sensors 135 to determine the locations of surfaces and theirrelative distance from the depth sensors 135.

The localization module 130 is connected to the canonical map 133 andMap 2. The localization module 130 repeatedly attempts to localize Map 2to the canonical map 133. The canonical map incorporator 132 isconnected to the canonical map 133 and Map 2. When the localizationmodule 130 localizes Map 2 to the canonical map 133, the canonical mapincorporator 132 incorporates the canonical map 133 into anchors of Map2. Map 2 is then updated with missing data that is included in thecanonical map.

The local content position system 134 is connected to Map 2. The localcontent position system 134 may, for example, be a system wherein a usercan locate local content in a particular location within a worldcoordinate frame. The local content then attaches itself to one anchorof Map 2. The local-to-world coordinate transformer 104 transforms thelocal coordinate frame to the world coordinate frame based on thesettings of the local content position system 134. The functioning ofthe rendering engine 30, display system 42, and data channel 62 havebeen described with reference to FIG. 2 .

The map publisher 136 uploads Map 2 to the server 20. The map storingroutine 118 of the server 20 then stores Map 2 within a storage mediumof the server 20.

The map merge algorithm 124 merges Map 2 with the canonical map 120.When more than two maps, for example, three or four maps relating to thesame or adjacent regions of the physical world, have been stored, themap merge algorithm 124 merges all the maps into the canonical map 120to render a new canonical map 120. The map transmitter 122 thentransmits the new canonical map 120 to any and all devices 12.1 and 12.2that are in an area represented by the new canonical map 120. When thedevices 12.1 and 12.2 localize their respective maps to the canonicalmap 120, the canonical map 120 becomes the promoted map.

FIG. 17 illustrates an example of generating key frames for a map of ascene, according to some embodiments. In the illustrated example, afirst key frame, KF1, is generated for a door on a left wall of theroom. A second key frame, KF2, is generated for an area in a cornerwhere a floor, the left wall, and a right wall of the room meet. A thirdkey frame, KF3, is generated for an area of a window on the right wallof the room. A fourth key frame, KF4, is generated for an area at a farend of a rug on a floor of the wall. A fifth key frame, KF5, isgenerated for an area of the rug closest to the user.

FIG. 18 illustrates an example of generating persistent poses for themap of FIG. 17 , according to some embodiments. In some embodiments, anew persistent pose is created when the device measures a thresholddistance traveled, and/or when an application requests a new persistentpose (PP). In some embodiments, the threshold distance may be 3 meters,5 meters, 20 meters, or any other suitable distance. Selecting a smallerthreshold distance (e.g., 1 m) may result in an increase in compute loadsince a larger number of PPs may be created and managed compared tolarger threshold distances. Selecting a larger threshold distance (e.g.40 m) may result in increased virtual content placement error since asmaller number of PPs would be created, which would result in fewer PCFsbeing created, which means the virtual content attached to the PCF couldbe a relatively large distance (e.g. 30 m) away from the PCF, and errorincreases with increasing distance from a PCF to the virtual content.

In some embodiments, a PP may be created at the start of a new session.This initial PP may be thought of as zero, and can be visualized as thecenter of a circle that has a radius equal to the threshold distance.When the device reaches the perimeter of the circle, and, in someembodiments, an application requests a new PP, a new PP may be placed atthe current location of the device (at the threshold distance). In someembodiments, a new PP will not be created at the threshold distance ifthe device is able to find an existing PP within the threshold distancefrom the device's new position. In some embodiments, when a new PP(e.g., PP1150 in FIG. 14 ) is created, the device attaches one or moreof the closest key frames to the PP. In some embodiments, the locationof the PP relative to the key frames may be based on the location of thedevice at the time a PP is created. In some embodiments, a PP will notbe created when the device travels a threshold distance unless anapplication requests a PP.

In some embodiments, an application may request a PCF from the devicewhen the application has virtual content to display to the user. The PCFrequest from the application may trigger a PP request, and a new PPwould be created after the device travels the threshold distance. FIG.18 illustrates a first persistent pose PP1 which may have the closestkey frames, (e.g. KF1, KF2, and KF3) attached by, for example, computingrelative poses between the key frames to the persistent pose. FIG. 18also illustrates a second persistent pose PP2 which may have the closestkey frames (e.g. KF4 and KF5) attached.

FIG. 19 illustrates an example of generating a PCF for the map of FIG.17 , according to some embodiments. In the illustrated example, a PCF 1may include PP1 and PP2. As described above, the PCF may be used fordisplaying image data relative to the PCF. In some embodiments, each PCFmay have coordinates in another coordinate frame (e.g., a worldcoordinate frame) and a PCF descriptor, for example, uniquelyidentifying the PCF. In some embodiments, the PCF descriptor may becomputed based on feature descriptors of features in frames associatedwith the PCF. In some embodiments, various constellations of PCFs may becombined to represent the real world in a persistent manner and thatrequires less data and less transmission of data.

FIGS. 20A to 20C are schematic diagrams illustrating an example ofestablishing and using a persistent coordinate frame. FIG. 20A shows twousers 4802A, 4802B with respective local tracking maps 4804A, 4804B thathave not localized to a canonical map. The origins 4806A, 4806B forindividual users are depicted by the coordinate system (e.g., a worldcoordinate system) in their respective areas. These origins of eachtracking map may be local to each user, as the origins are dependent onthe orientation of their respective devices when tracking was initiated.

As the sensors of the user device scan the environment, the device maycapture images that, as described above in connection with FIG. 14 , maycontain features representing persistent objects such that those imagesmay be classified as key frames, from which a persistent pose may becreated. In this example, the tracking map 4802A includes a persistentpose (PP) 4808A; the tracking 4802B includes a PP 4808B.

Also as described above in connection with FIG. 14 , some of the PP'smay be classified as PCFs which are used to determine the orientation ofvirtual content for rendering it to the user. FIG. 20B shows that XRdevices worn by respective users 4802A, 4802B may create local PCFs4810A, 4810B based on the PP 4808A, 4808B. FIG. 20C shows thatpersistent content 4812A, 4812B (e.g., a virtual content) may beattached to the PCFs 4810A, 4810B by respective XR devices.

In this example, virtual content may have a virtual content coordinateframe, that may be used by an application generating virtual content,regardless of how the virtual content should be displayed. The virtualcontent, for example, may be specified as surfaces, such as triangles ofa mesh, at particular locations and angles with respect to the virtualcontent coordinate frame. To render that virtual content to a user, thelocations of those surfaces may be determined with respect to the userthat is to perceive the virtual content.

Attaching virtual content to the PCFs may simplify the computationinvolved in determining locations of the virtual content with respect tothe user. The location of the virtual content with respect to a user maybe determined by applying a series of transformations. Some of thosetransformations may change, and may be updated frequently. Others ofthose transformations may be stable and may be updated in frequently ornot at all. Regardless, the transformations may be applied withrelatively low computational burden such that the location of thevirtual content can be updated with respect to the user frequently,providing a realistic appearance to the rendered virtual content.

In the example of FIGS. 20A-20C, user 1's device has a coordinate systemthat can be related to the coordinate system that defines the origin ofthe map by the transformation rig1_T_w1. User 2's device has a similartransformation rig2_T_w2. These transformations may be expressed as sixdegrees of transformation, specifying translation and rotation to alignthe device coordinate systems with the map coordinate systems. In someembodiments, the transformation may be expressed as two separatetransformations, one specifying translation and the other specifyingrotation. Accordingly, it should be appreciated that the transformationsmay be expressed in a form that simplifies computation or otherwiseprovides an advantage.

Transformations between the origins of the tracking maps and the PCFsidentified by the respective user devices are expressed as pcf1_T_w1 andpcf2_T_w2. In this example the PCF and the PP are identical, such thatthe same transformation also characterizes the PP's.

The location of the user device with respect to the PCF can therefore becomputed by the serial application of these transformations, such asrig1_T_pcf1=(rig1_T_w1)*(pcf1_T_w1).

As shown in FIG. 20C, the virtual content is locate with respect to thePCFs, with a transformation of obj1_T_pcf1. This transformation may beset by an application generating the virtual content that may receiveinformation from a world reconstruction system describing physicalobjects with respect to the PCF. To render the virtual content to theuser, a transformation to the coordinate system of the user's device iscomputed, which may be computed by relating the virtual contentcoordinate frame to the origin of the tracking map through thetransformation obj1_t_w1=(obj1_T_pcf1)*(pcf1_T_w1). That transformationmay then be related to the user's device through further transformationrig1_T_w1.

The location of the virtual content may change, based on output from anapplication generating the virtual content. When that changes, theend-to-end transformation, from a source coordinate system to adestination coordinate system, may be recomputed. Additionally, thelocation and/or headpose of the user may change as the user moves. As aresult, the transformation rig1_T_w1 may change, as would any end-to-endtransformation that depends on the location or headpose of the user.

The transformation rig1_T_w1 may be updated with motion of the userbased on tracking the position of the user with respect to stationaryobjects in the physical world. Such tracking may be performed by aheadphone tacking component processing a sequence of images, asdescribed above, or other component of the system. Such updates may bemade by determining pose of the user with respect to a stationary frameof reference, such as a PP.

In some embodiments, the location and orientation of a user device maybe determined relative to the nearest persistent pose, or, in thisexample, a PCF, as the PP is used as a PCF. Such a determination may bemade by identifying in current images captured with sensors on thedevice feature points that characterize the PP. Using image processingtechniques, such as stereoscopic image analysis, the location of thedevice with respect to those feature points may be determined. From thisdata, the system could calculate the change in transformation associatedwith the user's motions based on the relationshiprig1_T_pcf1=(rig1_T_w1)*(pcf1_T_w1).

A system may determine and apply transformations in an order that iscomputationally efficient. For example, the need to compute rig1_T_w1from a measurement yielding rig1_T_pcf1 might be avoided by trackingboth user pose and defining the location of virtual content relative tothe PP or a PCF built on a persistent pose. In this way thetransformation from a source coordinate system of the virtual content tothe destination coordinate system of the user's device may be based onthe measured transformation according to the expression(rig1_T_pcf1)*(obj1_t_pcf1), with the first transformation beingmeasured by the system and the latter transformation being supplied byan application specifying virtual content for rendering. In embodimentsin which the virtual content is positioned with respect to the origin ofthe map, the end-to-end transformation may relate the virtual objectcoordinate system to the PCF coordinate system based on a furthertransformation between the map coordinates and the PCF coordinates. Inembodiments in which the virtual content is positioned with respect to adifferent PP or PCF than the one against which user position is beingtracked, a transformation between the two may be applied. Such atransformation may be fixed and may be determined, for example, from amap in which both appear.

A transform-based approach may be implemented, for example, in a devicewith components that process sensor data to build a tracking map. Aspart of that process, those components may identify feature points thatmay be used as persistent poses, which in turn may be turned into PCFs.Those components may limit the number of persistent poses generated forthe map, to provide a suitable spacing between persistent poses, whileallowing the user, regardless of location in the physical environment,to be close enough to a persistent pose location to accurately computethe user's pose, as described above in connection with FIGS. 17-19 . Asthe closest persistent pose to a user is updated, as a result of usermovement, refinements to the tracking map or other causes, any of thetransformations that are used to compute the location of virtual contentrelative to the user that depend on the location of the PP (or PCF ifbeing used) may be updated and stored for use, at least until the usermoves away from that persistent pose. Nonetheless, by computing andstoring transformations, the computational burden each time the locationof virtual content is update may be relatively low, such that it may beperformed with relatively low latency.

FIGS. 20A-20C illustrate positioning with respect to a tracking map, andeach device had its own tracking map. However, transformations may begenerated with respect to any map coordinate system. Persistence ofcontent across user sessions of an XR system may be achieved by using apersistent map. Shared experiences of users may also be facilitated byusing a map to which multiple user devices may be oriented.

In some embodiments, described in greater detail below, the location ofvirtual content may be specified in relation to coordinates in acanonical map, formatted such that any of multiple devices may use themap. Each device might maintain a tracking map and may determine thechange of pose of the user with respect to the tracking map. In thisexample, a transformation between the tracking map and the canonical mapmay be determined through a process of “localization”—which may beperformed by matching structures in the tracking map (such as one ormore persistent poses) to one or more structures of the canonical map(such as one or more PCFs).

Described in greater below are techniques for creating and usingcanonical maps in this way.

Deep Key Frame

Techniques as described herein rely on comparison of image frames. Forexample, to establish the position of a device with respect to atracking map, a new image may be captured with sensors worn by the userand an XR system may search, in a set of images that were used to createthe tracking map, images that share at least a predetermined amount ofinterest points with the new image. As an example of another scenarioinvolving comparisons of image frames, a tracking map might be localizedto a canonical map by first finding image frames associated with apersistent pose in the tracking map that is similar to an image frameassociated with a PCF in the canonical map. Alternatively, atransformation between two canonical maps may be computed by firstfinding similar image frames in the two maps.

Deep key frames provide a way to reduce the amount of processingrequired to identify similar image frames. For example, in someembodiments, the comparison may be between image features in a new 2Dimage (e.g., “2D features”) and 3D features in the map. Such acomparison may be made in any suitable way, such as by projecting the 3Dimages into a 2D plane. A conventional method such as Bag of Words (BoW)searches the 2D features of a new image in a database including all 2Dfeatures in a map, which may require significant computing resourcesespecially when a map represents a large area. The conventional methodthen locates the images that share at least one of the 2D features withthe new image, which may include images that are not useful for locatingmeaningful 3D features in the map. The conventional method then locates3D features that are not meaningful with respect to the 2D features inthe new image.

The inventors have recognized and appreciated techniques to retrieveimages in the map using less memory resource (e.g., a quarter of thememory resource used by BoW), higher efficiency (e.g., 2.5 ms processingtime for each key frame, 100 μs for comparing against 500 key frames),and higher accuracy (e.g., 20% better retrieval recall than BoW for 1024dimensional model, 5% better retrieval recall than BoW for 256dimensional model).

To reduce computation, a descriptor may be computed for an image framethat may be used to compare an image frame to other image frames. Thedescriptors may be stored instead of or in addition to the image framesand feature points. In a map in which persistent poses and/or PCFs maybe generated from image frames, the descriptor of the image frame orframes from which each persistent pose or PCF was generated may bestored as part of the persistent pose and/or PCF.

In some embodiments, the descriptor may be computed as a function offeature points in the image frame. In some embodiments, a neural networkis configured to compute a unique frame descriptor to represent animage. The image may have a resolution higher than 1 Megabyte such thatenough details of a 3D environment within a field-of-view of a deviceworn by a user is captured in the image. The frame descriptor may bemuch shorter, such as a string of numbers, for example, in the range of128 Bytes to 512 Bytes or any number in between.

In some embodiments, the neural network is trained such that thecomputed frame descriptors indicate similarity between images. Images ina map may be located by identifying, in a database comprising imagesused to generate the map, the nearest images that may have framedescriptors within a predetermined distance to a frame descriptor for anew image. In some embodiments, the distances between images may berepresented by a difference between the frame descriptors of the twoimages.

FIG. 21 is a block diagram illustrating a system for generating adescriptor for an individual image, according to some embodiments. Inthe illustrated example, a frame embedding generator 308 is shown. Theframe embedding generator 308, in some embodiments, may be used withinthe server 20, but may alternatively or additionally execute in whole orin part within one of the XR devices 12.1 and 12.2, or any other deviceprocessing images for comparison to other images.

In some embodiments, the frame embedding generator may be configured togenerate a reduced data representation of an image from an initial size(e.g., 76,800 bytes) to a final size (e.g., 256 bytes) that isnonetheless indicative of the content in the image despite a reducedsize. In some embodiments, the frame embedding generator may be used togenerate a data representation for an image which may be a key frame ora frame used in other ways. In some embodiments, the frame embeddinggenerator 308 may be configured to convert an image at a particularlocation and orientation into a unique string of numbers (e.g., 256bytes). In the illustrated example, an image 320 taken by an XR devicemay be processed by feature extractor 324 to detect interest points 322in the image 320. Interest points may be or may not be derived fromfeature points identified as described above for features 1120 (FIG. 14) or as otherwise described herein. In some embodiments, interest pointsmay be represented by descriptors as described above for descriptors1130 (FIG. 14 ), which may be generated using a deep sparse featuremethod. In some embodiments, each interest point 322 may be representedby a string of numbers (e.g., 32 bytes). There may, for example, be nfeatures (e.g., 100) and each feature is represented by a string of 32bytes.

In some embodiments, the frame embedding generator 308 may include aneural network 326. The neural network 326 may include a multi-layerperceptron unit 312 and a maximum (max) pool unit 314. In someembodiments, the multi-layer perceptron (MLP) unit 312 may comprise amulti-layer perceptron, which may be trained. In some embodiments, theinterest points 322 (e.g., descriptors for the interest points) may bereduced by the multi-layer perceptron 312, and may output as weightedcombinations 310 of the descriptors. For example, the MLP may reduce nfeatures to m feature that is less than n features.

In some embodiments, the MLP unit 312 may be configured to performmatrix multiplication. The multi-layer perceptron unit 312 receives theplurality of interest points 322 of an image 320 and converts eachinterest point to a respective string of numbers (e.g., 256). Forexample, there may be 100 features and each feature may be representedby a string of 256 numbers. A matrix, in this example, may be createdhaving 100 horizontal rows and 256 vertical columns. Each row may have aseries of 256 numbers that vary in magnitude with some being smaller andothers being larger. In some embodiments, the output of the MLP may bean n×256 matrix, where n represents the number of interest pointsextracted from the image. In some embodiments, the output of the MLP maybe an m×256 matrix, where m is the number of interest points reducedfrom n.

In some embodiments, the MLP 312 may have a training phase, during whichmodel parameters for the MLP are determined, and a use phase. In someembodiments, the MLP may be trained as illustrated in FIG. 25 . Theinput training data may comprise data in sets of three, the set of threecomprising 1) a query image, 2) a positive sample, and 3) a negativesample. The query image may be considered the reference image.

In some embodiments, the positive sample may comprise an image that issimilar to the query image. For example, in some embodiments, similarmay be having the same object in both the query and positive sampleimage but viewed from a different angle. In some embodiments, similarmay be having the same object in both the query and positive sampleimages but having the object shifted (e.g., left, right, up, down)relative to the other image.

In some embodiments, the negative sample may comprise an image that isdissimilar to the query image. For example, in some embodiments, adissimilar image may not contain any objects that are prominent in thequery image or may contain only a small portion of a prominent object inthe query image (e.g., <10%, 1%). A similar image, in contrast, may havemost of an object (e.g. >50%, or >75%) in the query image, for example.

In some embodiments, interest points may be extracted from the images inthe input training data and may be converted to feature descriptors.These descriptors may be computed both for the training images as shownin FIG. 25 and for extracted features in operation of frame embeddinggenerator 308 of FIG. 21 . In some embodiments, a deep sparse feature(DSF) process may be used to generate the descriptors (e.g., DSFdescriptors) as described in U.S. patent application Ser. No.16/190,948. In some embodiments, DSF descriptors are n×32 dimension. Thedescriptors may then be passed through the model/MLP to create a 256byte output. In some embodiments, the model/MLP may have the samestructure as MLP 312 such that once the model parameters are set throughtraining, the resulting trained MLP may be used as MLP 312.

In some embodiments, the feature descriptors (e.g., the 256 byte outputfrom the MLP model) may then be sent to a triplet margin loss module(which may only be used during the training phase, not during use phaseof the MLP neural network). In some embodiments, the triplet margin lossmodule may be configured to select parameters for the model so as toreduce the difference between the 256 byte output from the query imageand the 256 byte output from the positive sample, and to increase thedifference between the 256 byte output from the query image and the 256byte output from the negative sample. In some embodiments, the trainingphase may comprise feeding a plurality of triplet input images into thelearning process to determine model parameters. This training processmay continue, for example, until the differences for positive images isminimized and the difference for negative images is maximized or untilother suitable exit criteria are reached.

Referring back to FIG. 21 , the frame embedding generator 308 mayinclude a pooling layer, here illustrated as maximum (max) pool unit314. The max pool unit 314 may analyze each column to determine amaximum number in the respective column. The max pool unit 314 maycombine the maximum value of each column of numbers of the output matrixof the MLP 312 into a global feature string 316 of, for example, 256numbers. It should be appreciated that images processed in XR systemsmight, desirably, have high-resolution frames, with potentially millionsof pixels. The global feature string 316 is a relatively small numberthat takes up relatively little memory and is easily searchable comparedto an image (e.g., with a resolution higher than 1 Megabyte). It is thuspossible to search for images without analyzing each original frame fromthe camera and it is also cheaper to store 256 bytes instead of completeframes.

FIG. 22 is a flow chart illustrating a method 2200 of computing an imagedescriptor, according to some embodiments. The method 2200 may startfrom receiving (Act 2202) a plurality of images captured by an XR deviceworn by a user. In some embodiments, the method 2200 may includedetermining (Act 2204) one or more key frames from the plurality ofimages. In some embodiments, Act 2204 may be skipped and/or may occurafter step 2210 instead.

The method 2200 may include identifying (Act 2206) one or more interestpoints in the plurality of images with an artificial neural network, andcomputing (Act 2208) feature descriptors for individual interest pointswith the artificial neural network. The method may include computing(Act 2210), for each image, a frame descriptor to represent the imagebased, at least in part, on the computed feature descriptors for theidentified interest points in the image with the artificial neuralnetwork.

FIG. 23 is a flow chart illustrating a method 2300 of localization usingimage descriptors, according to some embodiments. In this example, a newimage frame, depicting the current location of the XR device may becompared to image frames stored in connection with points in a map (suchas a persistent pose or a PCF as described above). The method 2300 maystart from receiving (Act 2302) a new image captured by an XR deviceworn by a user. The method 2300 may include identifying (Act 2304) oneor more nearest key frames in a database comprising key frames used togenerate one or more maps. In some embodiments, a nearest key frame maybe identified based on coarse spatial information and/or previouslydetermined spatial information. For example, coarse spatial informationmay indicate that the XR device is in a geographic region represented bya 50 m×50 m area of a map. Image matching may be performed only forpoints within that area. As another example, based on tracking, the XRsystem may know that an XR device was previously proximate a firstpersistent pose in the map and was moving in a direction of a secondpersistent pose in the map. That second persistent pose may beconsidered the nearest persistent pose and the key frame stored with itmay be regarded as the nearest key frame. Alternatively or additionally,other metadata, such as GPS data or WiFi fingerprints, may be used toselect a nearest key frame or set of nearest key frames.

Regardless of how the nearest key frames are selected, frame descriptorsmay be used to determine whether the new image matches any of the framesselected as being associated with a nearby persistent pose. Thedetermination may be made by comparing a frame descriptor of the newimage with frame descriptors of the closest key frames, or a subset ofkey frames in the database selected in any other suitable way, andselecting key frames with frame descriptors that are within apredetermined distance of the frame descriptor of the new image. In someembodiments, a distance between two frame descriptors may be computed byobtaining the difference between two strings of numbers that mayrepresent the two frame descriptors. In embodiments in which the stringsare processed as strings of multiple quantities, the difference may becomputed as a vector difference.

Once a matching image frame is identified, the orientation of the XRdevice relative to that image frame may be determined. The method 2300may include performing (Act 2306) feature matching against 3D featuresin the maps that correspond to the identified nearest key frames, andcomputing (Act 2308) pose of the device worn by the user based on thefeature matching results. In this way, the computationally intensivematching of features points in two images may be performed for as few asone image that has already been determined to be a likely match for thenew image.

FIG. 24 is a flow chart illustrating a method 2400 of training a neuralnetwork, according to some embodiments. The method 2400 may start fromgenerating (Act 2402) a dataset comprising a plurality of image sets.Each of the plurality of image sets may include a query image, apositive sample image, and a negative sample image. In some embodiments,the plurality of image sets may include synthetic recording pairsconfigured to, for example, teach the neural network basic informationsuch as shapes. In some embodiments, the plurality of image sets mayinclude real recording pairs, which may be recorded from a physicalworld.

In some embodiments, inliers may be computed by fitting a fundamentalmatrix between two images. In some embodiments, sparse overlap may becomputed as the intersection over union (IoU) of interest points seen inboth images. In some embodiments, a positive sample may include at leasttwenty interest points, serving as inliers, that are the same as in thequery image. A negative sample may include less than ten inlier points.A negative sample may have less than half of the sparse pointsoverlapping with the sparse points of the query image.

The method 2400 may include computing (Act 2404), for each image set, aloss by comparing the query image with the positive sample image and thenegative sample image. The method 2400 may include modifying (Act 2406)the artificial neural network based on the computed loss such that adistance between a frame descriptor generated by the artificial neuralnetwork for the query image and a frame descriptor for the positivesample image is less than a distance between the frame descriptor forthe query image and a frame descriptor for the negative sample image.

It should be appreciated that although methods and apparatus configuredto generate global descriptors for individual images are describedabove, methods and apparatus may be configured to generate descriptorsfor individual maps. For example, a map may include a plurality of keyframes, each of which may have a frame descriptor as described above. Amax pool unit may analyze the frame descriptors of the map's key framesand combines the frame descriptors into a unique map descriptor for themap.

Further, it should be appreciated that other architectures may be usedfor processing as described above. For example, separate neural networksare described for generating DSF descriptors and frame descriptors. Suchan approach is computationally efficient. However, in some embodiments,the frame descriptors may be generated from selected feature points,without first generating DSF descriptors.

Ranking and Merging Maps

Described herein are methods and apparatus for ranking and merging aplurality of environment maps in a cross reality system. Map merging mayenable maps representing overlapping portions of the physical world tobe combined to represent a larger area. Ranking maps may enableefficiently performing techniques as described herein, including mapmerging, that involve selecting a map from a set of maps based onsimilarity. In some embodiments, for example, a set of canonical mapsformatted in a way that they may be accessed by any of a number of XRdevices, may be maintained by the system. These canonical maps may beformed by merging selected tracking maps from those devices with othertracking maps or previously stored canonical maps. The canonical mapsmay be ranked, for example, for use in selecting one or more canonicalmaps to merge with a new tracking map and/or to select one or morecanonical maps from the set to use within a device.

To provide realistic XR experiences to users, the XR system must knowthe user's physical surroundings in order to correctly correlatelocations of virtual objects in relation to real objects. Informationabout a user's physical surroundings may be obtained from an environmentmap for the user's location.

The inventors have recognized and appreciated that an XR system couldprovide an enhanced XR experience to multiple users sharing a sameworld, comprising real and/or virtual content, by enabling efficientsharing of environment maps of the real/physical world collected bymultiple users, whether those users are present in the world at the sameor different times. However, there are significant challenges inproviding such a system. Such a system may store multiple maps generatedby multiple users and/or the system may store multiple maps generated atdifferent times. For operations that might be performed with apreviously generated map, such as localization, for example as describedabove, substantial processing may be required to identify a relevantenvironment map of a same world (e.g. same real world location) from allthe environment maps collected in an XR system. In some embodiments,there may only be a small number of environment maps a device couldaccess, for example for localization. In some embodiments, there may bea large number of environment maps a device could access. The inventorshave recognized and appreciated techniques to quickly and accuratelyrank the relevance of environment maps out of all possible environmentmaps, such as the universe of all canonical maps 120 in FIG. 28 , forexample. A high ranking map may then be selected for further processing,such as to render virtual objects on a user display realisticallyinteracting with the physical world around the user or merging map datacollected by that user with stored maps to create larger or moreaccurate maps.

In some embodiments, a stored map that is relevant to a task for a userat a location in the physical world may be identified by filteringstored maps based on multiple criteria. Those criteria may indicatecomparisons of a tracking map, generated by the wearable device of theuser in the location, to candidate environment maps stored in adatabase. The comparisons may be performed based on metadata associatedwith the maps, such as a Wi-Fi fingerprint detected by the devicegenerating the map and/or set of BSSID's to which the device wasconnected while forming the map. The comparisons may also be performedbased on compressed or uncompressed content of the map. Comparisonsbased on a compressed representation may be performed, for example, bycomparison of vectors computed from map content. Comparisons based onun-compressed maps may be performed, for example, by localizing thetracking map within the stored map, or vice versa. Multiple comparisonsmay be performed in an order based on computation time needed to reducethe number of candidate maps for consideration, with comparisonsinvolving less computation being performed earlier in the order thanother comparisons requiring more computation.

FIG. 26 depicts an AR system 800 configured to rank and merge one ormore environment maps, according to some embodiments. The AR system mayinclude a passable world model 802 of an AR device. Information topopulate the passable world model 802 may come from sensors on the ARdevice, which may include computer executable instructions stored in aprocessor 804 (e.g., a local data processing module 570 in FIG. 4 ),which may perform some or all of the processing to convert sensor datainto a map. Such a map may be a tracking map, as it can be built assensor data is collected as the AR device operates in a region. Alongwith that tracking map, area attributes may be supplied so as toindicate the area that the tracking map represents. These areaattributes may be a geographic location identifier, such as coordinatespresented as latitude and longitude or an ID used by the AR system torepresent a location. Alternatively or additionally, the area attributesmay be measured characteristics that have a high likelihood of beingunique for that area. The area attributes, for example, may be derivedfrom parameters of wireless networks detected in the area. In someembodiments, the area attribute may be associated with a unique addressof an access-point the AR system is nearby and/or connected to. Forexample, the area attribute may be associated with a MAC address orbasic service set identifiers (BSSIDs) of a 5G base station/router, aWi-Fi router, and the like.

In the example of FIG. 26 , the tracking maps may be merged with othermaps of the environment. A map rank portion 806 receives tracking mapsfrom the device PW 802 and communicates with a map database 808 toselect and rank environment maps from the map database 808. Higherranked, selected maps are sent to a map merge portion 810.

The map merge portion 810 may perform merge processing on the maps sentfrom the map rank portion 806. Merge processing may entail merging thetracking map with some or all of the ranked maps and transmitting thenew, merged maps to a passable world model 812. The map merge portionmay merge maps by identifying maps that depict overlapping portions ofthe physical world. Those overlapping portions may be aligned such thatinformation in both maps may be aggregated into a final map. Canonicalmaps may merged with other canonical maps and/or tracking maps.

The aggregation may entail extending one map with information fromanother map. Alternatively or additionally, aggregation may entailadjusting the representation of the physical world in one map, based oninformation in another map. A later map, for example, may reveal thatobjects giving rise to feature points have moved, such that the map maybe updated based on later information. Alternatively, two maps maycharacterize the same region with different feature points andaggregating may entail selecting a set of feature points from the twomaps to better represent that region. Regardless of the specificprocessing that occurs in the merging process, in some embodiments, PCFsfrom all maps that are merged may be retained, such that applicationspositioning content with respect to them may continue to do so. In someembodiments, merging of maps may result in redundant persistent poses,and some of the persistent poses may be deleted. When a PCF isassociated with a persistent pose that is to be deleted, merging mapsmay entail modifying the PCF to be associated with a persistent poseremaining in the map after merging.

In some embodiments, as maps are extended and or updated, they may berefined. Refinement may entail computation to reduce internalinconsistency between feature points that likely represent the sameobject in the physical world. Inconsistency may result from inaccuraciesin the poses associated with key frames supplying feature points thatrepresent the same objects in the physical world. Such inconsistency mayresult, for example, from an XR device computing poses relative to atracking map, which in turn is built based on estimating poses, suchthat errors in pose estimation accumulate, creating a “drift” in poseaccuracy over time. By performing a bundle adjustment or other operationto reduce inconsistencies of the feature points from multiple keyframes, the map may be refined.

Upon a refinement, the location of a persistent point relative to theorigin of a map may change. Accordingly, the transformation associatedwith that persistent point, such as a persistent pose or a PCF, maychange. In some embodiments, the XR system, in connection with maprefinement (whether as part of a merge operation or performed for otherreasons) may re-compute transformations associated with any persistentpoints that have changed. These transformations might be pushed from acomponent computing the transformations to a component using thetransformation such that any uses of the transformations may be based onthe updated location of the persistent points.

Passable world model 812 may be a cloud model, which may be shared bymultiple AR devices. Passable world model 812 may store or otherwisehave access to the environment maps in map database 808. In someembodiments, when a previously computed environment map is updated, theprior version of that map may be deleted so as to remove out of datemaps from the database. In some embodiments, when a previously computedenvironment map is updated, the prior version of that map may bearchived enabling retrieving/viewing prior versions of an environment.In some embodiments, permissions may be set such that only AR systemshaving certain read/write access may trigger prior versions of mapsbeing deleted/archived.

These environment maps created from tracking maps supplied by one ormore AR devices/systems may be accessed by AR devices in the AR system.The map rank portion 806 also may be used in supplying environment mapsto an AR device. The AR device may send a message requesting anenvironment map for its current location, and map rank portion 806 maybe used to select and rank environment maps relevant to the requestingdevice.

In some embodiments, the AR system 800 may include a downsample portion814 configured to receive the merged maps from the cloud PW 812. Thereceived merged maps from the cloud PW 812 may be in a storage formatfor the cloud, which may include high resolution information, such as alarge number of PCFs per square meter or multiple image frames or alarge set of feature points associated with a PCF. The downsampleportion 814 may be configured to downsample the cloud format maps to aformat suitable for storage on AR devices. The device format maps mayhave less data, such as fewer PCFs or less data stored for each PCF toaccommodate the limited local computing power and storage space of ARdevices.

FIG. 27 is a simplified block diagram illustrating a plurality ofcanonical maps 120 that may be stored in a remote storage medium, forexample, a cloud. Each canonical map 120 may include a plurality ofcanonical map identifiers indicating the canonical map's location withina physical space, such as somewhere on the planet earth. These canonicalmap identifiers may include one or more of the following identifiers:area identifiers represented by a range of longitudes and latitudes,frame descriptors (e.g., global feature string 316 in FIG. 21 ), Wi-Fifingerprints, feature descriptors (e.g., feature descriptors 310 in FIG.21 ), and device identities indicating one or more devices thatcontributed to the map. In the illustrated example, the canonical maps120 are disposed geographically in a two-dimensional pattern as they mayexist on a surface of the earth. The canonical maps 120 may be uniquelyidentifiable by corresponding longitudes and latitudes because anycanonical maps that have overlapping longitudes and latitudes may bemerged into a new canonical map.

FIG. 28 is a schematic diagram illustrating a method of selectingcanonical maps, which may be used for localizing a new tracking map toone or more canonical maps, according to some embodiment. The method maystart from accessing (Act 120) a universe of canonical maps 120, whichmay be stored, as an example, in a database in a passable world (e.g.,the passable world module 538). The universe of canonical maps mayinclude canonical maps from all previously visited locations. An XRsystem may filter the universe of all canonical maps to a small subsetor just a single map. It should be appreciated that, in someembodiments, it may not be possible to send all the canonical maps to aviewing device due to bandwidth restrictions. Selecting a subsetselected as being likely candidates for matching the tracking map tosend to the device may reduce bandwidth and latency associated withaccessing a remote database of maps.

The method may include filtering (Act 300) the universe of canonicalmaps based on areas with predetermined size and shapes. In theillustrated example in FIG. 27 , each square may represent an area. Eachsquare may cover 50 m×50 m. Each square may have six neighboring areas.In some embodiments, Act 300 may select at least one matching canonicalmap 120 covering longitude and latitude that include that longitude andlatitude of the position identifier received from an XR device, as longas at least one map exists at that longitude and latitude. In someembodiments, the Act 300 may select at least one neighboring canonicalmap covering longitude and latitude that are adjacent the matchingcanonical map. In some embodiments, the Act 300 may select a pluralityof matching canonical maps and a plurality of neighboring canonicalmaps. The Act 300 may, for example, reduce the number of canonical mapsapproximately ten times, for example, from thousands to hundreds to forma first filtered selection. Alternatively or additionally, criteriaother than latitude and longitude may be used to identify neighboringmaps. An XR device, for example, may have previously localized with acanonical map in the set as part of the same session. A cloud servicemay retain information about the XR device, including maps previouslylocalized to. In this example, the maps selected at Act 300 may includethose that cover an area adjacent to the map to which the XR devicelocalized to.

The method may include filtering (Act 302) the first filtered selectionof canonical maps based on Wi-Fi fingerprints. The Act 302 may determinea latitude and longitude based on a Wi-Fi fingerprint received as partof the position identifier from an XR device. The Act 302 may comparethe latitude and longitude from the Wi-Fi fingerprint with latitude andlongitude of the canonical maps 120 to determine one or more canonicalmaps that form a second filtered selection. The Act 302 may reduce thenumber of canonical maps approximately ten times, for example, fromhundreds to tens of canonical maps (e.g., 50) that form a secondselection For example, a first filtered selection may include 130canonical maps and the second filtered selection may include 50 of the130 canonical maps and may not include the other 80 of the 130 canonicalmaps.

The method may include filtering (Act 304) the second filtered selectionof canonical maps based on key frames. The Act 304 may compare datarepresenting an image captured by an XR device with data representingthe canonical maps 120. In some embodiments, the data representing theimage and/or maps may include feature descriptors (e.g., DSF descriptorsin FIG. 25 ) and/or global feature strings (e.g., 316 in FIG. 21 ). TheAct 304 may provide a third filtered selection of canonical maps. Insome embodiments, the output of Act 304 may only be five of the 50canonical maps identified following the second filtered selection, forexample. The map transmitter 122 then transmits the one or morecanonical maps based on the third filtered selection to the viewingdevice. The Act 304 may reduce the number of canonical maps forapproximately ten times, for example, from tens to single digits ofcanonical maps (e.g., 5) that form a third selection. In someembodiments, an XR device may receive canonical maps in the thirdfiltered selection, and attempt to localize into the received canonicalmaps.

For example, the Act 304 may filter the canonical maps 120 based on theglobal feature strings 316 of the canonical maps 120 and the globalfeature string 316 that is based on an image that is captured by theviewing device (e.g. an image that may be part of the local tracking mapfor a user). Each one of the canonical maps 120 in FIG. 27 thus has oneor more global feature strings 316 associated therewith. In someembodiments, the global feature strings 316 may be acquired when an XRdevice submits images or feature details to the cloud and the cloudprocesses the image or feature details to generate global featurestrings 316 for the canonical maps 120.

In some embodiments, the cloud may receive feature details of alive/new/current image captured by a viewing device, and the cloud maygenerate a global feature string 316 for the live image. The cloud maythen filter the canonical maps 120 based on the live global featurestring 316. In some embodiments, the global feature string may begenerated on the local viewing device. In some embodiments, the globalfeature string may be generated remotely, for example on the cloud. Insome embodiments, a cloud may transmit the filtered canonical maps to anXR device together with the global feature strings 316 associated withthe filtered canonical maps. In some embodiments, when the viewingdevice localizes its tracking map to the canonical map, it may do so bymatching the global feature strings 316 of the local tracking map withthe global feature strings of the canonical map.

It should be appreciated that an operation of an XR device may notperform all of the Acts (300, 302, 304). For example, if a universe ofcanonical maps are relatively small (e.g., 500 maps), an XR deviceattempting to localize may filter the universe of canonical maps basedon Wi-Fi fingerprints (e.g., Act 302) and Key Frame (e.g., Act 304), butomit filtering based on areas (e.g., Act 300). Moreover, it is notnecessary that maps in their entireties be compared. In someembodiments, for example, a comparison of two maps may result inidentifying common persistent points, such as persistent poses or PCFsthat appear in both the new map the selected map from the universe ofmaps. In that case, descriptors may be associated with persistentpoints, and those descriptors may be compared.

FIG. 29 is flow chart illustrating a method 900 of selecting one or moreranked environment maps, according to some embodiments. In theillustrated embodiment, the ranking is performed for a user's AR devicethat is creating a tracking map. Accordingly, the tracking map isavailable for use in ranking environment maps. In embodiments in whichthe tracking map is not available, some or all of portions of theselection and ranking of environment maps that do not expressly rely onthe tracking map may be used.

The method 900 may start at Act 902, where a set of maps from a databaseof environment maps (which may be formatted as canonical maps) that arein the neighborhood of the location where the tracking map was formedmay be accessed and then filtered for ranking. Additionally, at Act 902,at least one area attribute for the area in which the user's AR deviceis operating is determined. In scenarios in which the user's AR deviceis constructing a tracking map, the area attributes may correspond tothe area over which the tracking map was created. As a specific example,the area attributes may be computed based on received signals fromaccess points to computer networks while the AR device was computing thetracking map.

FIG. 30 depicts an exemplary map rank portion 806 of the AR system 800,according to some embodiments. The map rank portion 806 may be executingin a cloud computing environment, as it may include portions executingon AR devices and portions executing on a remote computing system suchas a cloud. The map rank portion 806 may be configured to perform atleast a portion of the method 900.

FIG. 31A depicts an example of area attributes AA1-AA8 of a tracking map(TM) 1102 and environment maps CM1-CM4 in a database, according to someembodiments. As illustrated, an environment map may be associated tomultiple area attributes. The area attributes AA1-AA8 may includeparameters of wireless networks detected by the AR device computing thetracking map 1102, for example, basic service set identifiers (BSSIDs)of networks to which the AR device are connected and/or the strength ofthe received signals of the access points to the wireless networksthrough, for example, a network tower 1104. The parameters of thewireless networks may comply with protocols including Wi-Fi and 5G NR.In the example illustrated in FIG. 32 , the area attributes are afingerprint of the area in which the user AR device collected sensordata to form the tracking map.

FIG. 31B depicts an example of the determined geographic location 1106of the tracking map 1102, according to some embodiments. In theillustrated example, the determined geographic location 1106 includes acentroid point 1110 and an area 1108 circling around the centroid point.It should be appreciated that the determination of a geographic locationof the present application is not limited to the illustrated format. Adetermined geographic location may have any suitable formats including,for example, different area shapes. In this example, the geographiclocation is determined from area attributes using a database relatingarea attributes to geographic locations. Databases are commerciallyavailable, for example, databases that relate Wi-Fi fingerprints tolocations expressed as latitude and longitude and may be used for thisoperation.

In the embodiment of FIG. 29 , a map database, containing environmentmaps may also include location data for those maps, including latitudeand longitude covered by the maps. Processing at Act 902 may entailselecting from that database a set of environment maps that covers thesame latitude and longitude determined for the area attributes of thetracking map.

Act 904 is a first filtering of the set of environment maps accessed inAct 902. In Act 902, environment maps are retained in the set based onproximity to the geolocation of the tracking map. This filtering stepmay be performed by comparing the latitude and longitude associated withthe tracking map and the environment maps in the set.

FIG. 32 depicts an example of Act 904, according to some embodiments.Each area attribute may have a corresponding geographic location 1202.The set of environment maps may include the environment maps with atleast one area attribute that has a geographic location overlapping withthe determined geographic location of the tracking map. In theillustrated example, the set of identified environment maps includesenvironment maps CM1, CM2, and CM4, each of which has at least one areaattribute that has a geographic location overlapping with the determinedgeographic location of the tracking map 1102. The environment map CM3associated with the area attribute AA6 is not included in the setbecause it is outside the determined geographic location of the trackingmap.

Other filtering steps may also be performed on the set of environmentmaps to reduce/rank the number of environment maps in the set that isultimately processed (such as for map merge or to provide passable worldinformation to a user device). The method 900 may include filtering (Act906) the set of environment maps based on similarity of one or moreidentifiers of network access points associated with the tracking mapand the environment maps of the set of environment maps. During theformation of a map, a device collecting sensor data to generate the mapmay be connected to a network through a network access point, such asthrough Wi-Fi or similar wireless communication protocol. The accesspoints may be identified by BSSID. The user device may connect tomultiple different access points as it moves through an area collectingdata to form a map. Likewise, when multiple devices supply informationto form a map, the devices may have connected through different accesspoints, so there may be multiple access points used in forming the mapfor that reason too. Accordingly, there may be multiple access pointsassociated with a map, and the set of access points may be an indicationof location of the map. Strength of signals from an access point, whichmay be reflected as an RSSI value, may provide further geographicinformation. In some embodiments, a list of BSSID and RSSI values mayform the area attribute for a map.

In some embodiments, filtering the set of environment maps based onsimilarity of the one or more identifiers of the network access pointsmay include retaining in the set of environment maps environment mapswith the highest Jaccard similarity to the at least one area attributeof the tracking map based on the one or more identifiers of networkaccess points. FIG. 33 depicts an example of Act 906, according to someembodiments. In the illustrated example, a network identifier associatedwith the area attribute AA7 may be determined as the identifier for thetracking map 1102. The set of environment maps after Act 906 includesenvironment map CM2, which may have area attributes within higherJaccard similarity to AA7, and environment map CM4, which also includethe area attributes AA7. The environment map CM1 is not included in theset because it has the lowest Jaccard similarity to AA7.

Processing at Acts 902-906 may be performed based on metadata associatedwith maps and without actually accessing the content of the maps storedin a map database. Other processing may involve accessing the content ofthe maps. Act 908 indicates accessing the environment maps remaining inthe subset after filtering based on metadata. It should be appreciatedthat this act may be performed either earlier or later in the process,if subsequent operations can be performed with accessed content.

The method 900 may include filtering (Act 910) the set of environmentmaps based on similarity of metrics representing content of the trackingmap and the environment maps of the set of environment maps. The metricsrepresenting content of the tracking map and the environment maps mayinclude vectors of values computed from the contents of the maps. Forexample, the Deep Key Frame descriptor, as described above, computed forone or more key frames used in forming a map may provide a metric forcomparison of maps, or portions of maps. The metrics may be computedfrom the maps retrieved at Act 908 or may be pre-computed and stored asmetadata associated with those maps. In some embodiments, filtering theset of environment maps based on similarity of metrics representingcontent of the tracking map and the environment maps of the set ofenvironment maps, may include retaining in the set of environment mapsenvironment maps with the smallest vector distance between a vector ofcharacteristics of the tracking map and vectors representing environmentmaps in the set of environment maps.

The method 900 may include further filtering (Act 912) the set ofenvironment maps based on degree of match between a portion of thetracking map and portions of the environment maps of the set ofenvironment maps. The degree of match may be determined as a part of alocalization process. As a non-limiting example, localization may beperformed by identifying critical points in the tracking map and theenvironment map that are sufficiently similar as they could representthe same portion of the physical world. In some embodiments, thecritical points may be features, feature descriptors, key frames/keyrigs, persistent poses, and/or PCFs. The set of critical points in thetracking map might then be aligned to produce a best fit with the set ofcritical points in the environment map. A mean square distance betweenthe corresponding critical points might be computed and, if below athreshold for a particular region of the tracking map, used as anindication that the tracking map and the environment map represent thesame region of the physical world.

In some embodiments, filtering the set of environment maps based ondegree of match between a portion of the tracking map and portions ofthe environment maps of the set of environment maps may includecomputing a volume of a physical world represented by the tracking mapthat is also represented in an environment map of the set of environmentmaps, and retaining in the set of environment maps environment maps witha larger computed volume than environment maps filtered out of the set.FIG. 34 depicts an example of Act 912, according to some embodiments. Inthe illustrated example, the set of environment maps after Act 912includes environment map CM4, which has an area 1402 matched with anarea of the tracking map 1102. The environment map CM1 is not includedin the set because it has no area matched with an area of the trackingmap 1102.

In some embodiments, the set of environment maps may be filtered in theorder of Act 906, Act 910, and Act 912. In some embodiments, the set ofenvironment maps may be filtered based on Act 906, Act 910, and Act 912,which may be performed in an order based on processing required toperform the filtering, from lowest to highest. The method 900 mayinclude loading (Act 914) the set of environment maps and data.

In the illustrated example, a user database stores area identitiesindicating areas that AR devices were used in. The area identities maybe area attributes, which may include parameters of wireless networksdetected by the AR devices when in use. A map database may storemultiple environment maps constructed from data supplied by the ARdevices and associated metadata. The associated metadata may includearea identities derived from the area identities of the AR devices thatsupplied data from which the environment maps were constructed. An ARdevice may send a message to a PW module indicating a new tracking mapis created or being created. The PW module may compute area identifiersfor the AR device and updates the user database based on the receivedparameters and/or the computed area identifiers. The PW module may alsodetermine area identifiers associated with the AR device requesting theenvironment maps, identify sets of environment maps from the mapdatabase based on the area identifiers, filter the sets of environmentmaps, and transmit the filtered sets of environment maps to the ARdevices. In some embodiments, the PW module may filter the sets ofenvironment maps based on one or more criteria including, for example, ageographic location of the tracking map, similarity of one or moreidentifiers of network access points associated with the tracking mapand the environment maps of the set of environment maps, similarity ofmetrics representing contents of the tracking map and the environmentmaps of the set of environment maps, and degree of match between aportion of the tracking map and portions of the environment maps of theset of environment maps.

Having thus described several aspects of some embodiments, it is to beappreciated that various alterations, modifications, and improvementswill readily occur to those skilled in the art. As one example,embodiments are described in connection with an augmented (AR)environment. It should be appreciated that some or all of the techniquesdescribed herein may be applied in an MR environment or more generallyin other XR environments, and in VR environments.

As another example, embodiments are described in connection withdevices, such as wearable devices. It should be appreciated that some orall of the techniques described herein may be implemented via networks(such as cloud), discrete applications, and/or any suitable combinationsof devices, networks, and discrete applications.

Further, FIG. 29 provides examples of criteria that may be used tofilter candidate maps to yield a set of high ranking maps. Othercriteria may be used instead of or in addition to the describedcriteria. For example, if multiple candidate maps have similar values ofa metric used for filtering out less desirable maps, characteristics ofthe candidate maps may be used to determine which maps are retained ascandidate maps or filtered out. For example, larger or more densecandidate maps may be prioritized over smaller candidate maps. In someembodiments, FIGS. 27-28 may describe all or part of the systems andmethods described in FIGS. 29-34 .

FIGS. 35 and 36 are schematic diagrams illustrating an XR systemconfigured to rank and merge a plurality of environment maps, accordingto some embodiments. In some embodiments, a passable world (PW) maydetermine when to trigger ranking and/or merging the maps. In someembodiments, determining a map to be used may be based at least partlyon deep key frames described above in relation to FIGS. 21-25 ,according to some embodiments.

FIG. 37 is a block diagram illustrating a method 3700 of creatingenvironment maps of a physical world, according to some embodiments. Themethod 3700 may start from localizing (Act 3702) a tracking map capturedby an XR device worn by a user to a group of canonical maps (e.g.,canonical maps selected by the method of FIG. 28 and/or the method 900of FIG. 29 ). The Act 3702 may include localizing keyrigs of thetracking map into the group of canonical maps. The localization resultof each keyrig may include the keyrig's localized pose and a set of2D-to-3D feature correspondences.

In some embodiments, the method 3700 may include splitting (Act 3704) atracking map into connected components, which may enable merging mapsrobustly by merging connected pieces. Each connected component mayinclude keyrigs that are within a predetermined distance. The method3700 may include merging (Act 3706) the connected components that arelarger than a predetermined threshold into one or more canonical maps,and removing the merged connected components from the tracking map.

In some embodiments, the method 3700 may include merging (Act 3708)canonical maps of the group that are merged with the same connectedcomponents of the tracking map. In some embodiments, the method 3700 mayinclude promoting (Act 3710) the remaining connected components of thetracking map that has not been merged with any canonical maps to be acanonical map. In some embodiments, the method 3700 may include merging(Act 3712) persistent poses and/or PCFs of the tracking maps and thecanonical maps that are merged with at least one connected component ofthe tracking map. In some embodiments, the method 3700 may includefinalizing (Act 3714) the canonical maps by, for example, fusing mappoints and pruning redundant keyrigs.

FIGS. 38A and 38B illustrate an environment map 3800 created by updatinga canonical map 700, which may be promoted from the tracking map 700(FIG. 7 ) with a new tracking map, according to some embodiments. Asillustrated and described with respect to FIG. 7 , the canonical map 700may provide a floor plan 706 of reconstructed physical objects in acorresponding physical world, represented by points 702. In someembodiments, a map point 702 may represent a feature of a physicalobject that may include multiple features. A new tracking map may becaptured about the physical world and uploaded to a cloud to merge withthe map 700. The new tracking map may include map points 3802, andkeyrigs 3804, 3806. In the illustrated example, keyrigs 3804 representkeyrigs that are successfully localized to the canonical map by, forexample, establishing a correspondence with a keyrig 704 of the map 700(as illustrated in FIG. 38B). On the other hand, keyrigs 3806 representkeyrigs that have not been localized to the map 700. Keyrigs 3806 may bepromoted to a separate canonical map in some embodiments.

FIGS. 39A to 39F are schematic diagrams illustrating an example of acloud-based persistent coordinate system providing a shared experiencefor users in the same physical space. FIG. 39A shows that a canonicalmap 4814, for example, from a cloud, is received by the XR devices wornby the users 4802A and 4802B of FIGS. 20A-20C. The canonical map 4814may have a canonical coordinate frame 4806C. The canonical map 4814 mayhave a PCF 4810C with a plurality of associated PPs (e.g., 4818A, 4818Bin FIG. 39C).

FIG. 39B shows that the XR devices established relationships betweentheir respective world coordinate system 4806A, 4806B with the canonicalcoordinate frame 4806C. This may be done, for example, by localizing tothe canonical map 4814 on the respective devices. Localizing thetracking map to the canonical map may result, for each device, atransformation between its local world coordinate system and thecoordinate system of the canonical map.

FIG. 39C shows that, as a result of localization, a transformation canbe computed (e.g., transformation 4816A, 4816B) between a local PCF(e.g., PCFs 4810A, 4810B) on the respective device to a respectivepersistent pose (e.g., PPs 4818A, 4818B) on the canonical map. Withthese transformations, each device may use its local PCFs, which can bedetected locally on the device by processing images detected withsensors on the device, to determine where with respect to the localdevice to display virtual content attached to the PPs 4818A, 4818B orother persistent points of the canonical map. Such an approach mayaccurately position virtual content with respect to each user and mayenable each user to have the same experience of the virtual content inthe physical space.

FIG. 39D shows a persistent pose snapshot from the canonical map to thelocal tracking maps. As can be seen, the local tracking maps areconnected to one another via the persistent poses. FIG. 39E shows thatthe PCF 4810A on the device worn by the user 4802A is accessible in thedevice worn by the user 4802B through PPs 4818A. FIG. 39F shows that thetracking maps 4804A, 4804B and the canonical 4814 may merge. In someembodiments, some PCFs may be removed as a result of merging. In theillustrated example, the merged map includes the PCF 4810C of thecanonical map 4814 but not the PCFs 4810A, 4810B of the tracking maps4804A, 4804B. The PPs previously associated with the PCFs 4810A, 4810Bmay be associated with the PCF 4810C after the maps merge.

EXAMPLES

FIGS. 40 and 41 illustrate an example of using a tracking map by thefirst XR device 12.1 of FIG. 9 . FIG. 40 is a two-dimensionalrepresentation of a three-dimensional first local tracking map (Map 1),which may be generated by the first XR device of FIG. 9 , according tosome embodiments. FIG. 41 is a block diagram illustrating uploading Map1 from the first XR device to the server of FIG. 9 , according to someembodiments.

FIG. 40 illustrates Map 1 and virtual content (Content123 andContent456) on the first XR device 12.1. Map 1 has an origin (Origin 1).Map 1 includes a number of PCFs (PCF a to PCF d). From the perspectiveof the first XR device 12.1, PCF a, by way of example, is located at theorigin of Map 1 and has X, Y, and Z coordinates of (0,0,0) and PCF b hasX, Y, and Z coordinates (−1,0,0). Content123 is associated with PCF a.In the present example, Content123 has an X, Y, and Z relationshiprelative to PCF a of (1,0,0). Content456 has a relationship relative toPCF b. In the present example, Content456 has an X, Y, and Zrelationship of (1,0,0) relative to PCF b.

In FIG. 41 , the first XR device 12.1 uploads Map 1 to the server 20. Inthis example, as the server stores no canonical map for the same regionof the physical world represented by the tracking map, and the trackingmap is stored as an initial canonical map. The server 20 now has acanonical map based on Map 1. The first XR device 12.1 has a canonicalmap that is empty at this stage. The server 20, for purposes ofdiscussion, and in some embodiments, includes no other maps other thanMap 1. No maps are stored on the second XR device 12.2.

The first XR device 12.1 also transmits its Wi-Fi signature data to theserver 20. The server 20 may use the Wi-Fi signature data to determine arough location of the first XR device 12.1 based on intelligencegathered from other devices that have, in the past, connected to theserver 20 or other servers together with the GPS locations of such otherdevices that have been recorded. The first XR device 12.1 may now endthe first session (See FIG. 8 ) and may disconnect from the server 20.

FIG. 42 is a schematic diagram illustrating the XR system of FIG. 16 ,showing the second user 14.2 has initiated a second session using asecond XR device of the XR system after the first user 14.1 hasterminated a first session, according to some embodiments. FIG. 43A is ablock diagram showing the initiation of a second session by a seconduser 14.2. The first user 14.1 is shown in phantom lines because thefirst session by the first user 14.1 has ended. The second XR device12.2 begins to record objects. Various systems with varying degrees ofgranulation may be used by the server 20 to determine that the secondsession by the second XR device 12.2 is in the same vicinity of thefirst session by the first XR device 12.1. For example, Wi-Fi signaturedata, global positioning system (GPS) positioning data, GPS data basedon Wi-Fi signature data, or any other data that indicates a location maybe included in the first and second XR devices 12.1 and 12.2 to recordtheir locations. Alternatively, the PCFs that are identified by thesecond XR device 12.2 may show a similarity to the PCFs of Map 1.

As shown in FIG. 43B, the second XR device boots up and begins tocollect data, such as images 1110 from one or more cameras 44, 46. Asshown in FIG. 14 , in some embodiments, an XR device (e.g. the second XRdevice 12.2) may collect one or more images 1110 and perform imageprocessing to extract one or more features/interest points 1120. Eachfeature may be converted to a descriptor 1130. In some embodiments, thedescriptors 1130 may be used to describe a key frame 1140, which mayhave the position and direction of the associated image attached. One ormore key frames 1140 may correspond to a single persistent pose 1150,which may be automatically generated after a threshold distance from theprevious persistent pose 1150, e.g., 3 meters. One or more persistentposes 1150 may correspond to a single PCF 1160, which may beautomatically generated after a pre-determined distance, e.g. every 5meters. Over time as the user continues to move around the user'senvironment, and the XR device continues to collect more data, such asimages 1110, additional PCFs (e.g., PCF 3 and PCF 4, 5) may be created.One or more applications 1180 may run on the XR device and providevirtual content 1170 to the XR device for presentation to the user. Thevirtual content may have an associated content coordinate frame whichmay be placed relative to one or more PCFs. As shown in FIG. 43B, thesecond XR device 12.2 creates three PCFs. In some embodiments, thesecond XR device 12.2 may try to localize into one or more canonicalmaps stored on the server 20.

In some embodiments, as shown in FIG. 43C, the second XR device 12.2 maydownload the canonical map 120 from the server 20. Map 1 on the secondXR device 12.2 includes PCFs a to d and Origin 1. In some embodiments,the server 20 may have multiple canonical maps for various locations andmay determine that the second XR device 12.2 is in the same vicinity asthe vicinity of the first XR device 12.1 during the first session andsends the second XR device 12.2 the canonical map for that vicinity.

FIG. 44 shows the second XR device 12.2 beginning to identify PCFs forpurposes of generating Map 2. The second XR device 12.2 has onlyidentified a single PCF, namely PCF 1,2. The X, Y, and Z coordinates ofPCF 1,2 for the second XR device 12.2 may be (1,1,1). Map 2 has its ownorigin (Origin 2), which may be based on the headpose of device 2 atdevice start-up for the current headpose session. In some embodiments,the second XR device 12.2 may immediately attempt to localize Map 2 tothe canonical map. In some embodiments, Map 2 may not be able tolocalize into Canonical Map (Map 1) (i.e. localization may fail) becausethe system does not recognize any or enough overlap between the twomaps. Localization may be performed by identifying a portion of thephysical world represented in a first map that is also represented in asecond map, and computing a transformation between the first map and thesecond map required to align those portions. In some embodiments, thesystem may localize based on PCF comparison between the local andcanonical maps. In some embodiments, the system may localize based onpersistent pose comparison between the local and canonical maps. In someembodiments, the system may localize based on key frame comparisonbetween the local and canonical maps.

FIG. 45 shows Map 2 after the second XR device 12.2 has identifiedfurther PCFs (PCF 1,2, PCF 3, PCF 4,5) of Map 2. The second XR device12.2 again attempts to localize Map 2 to the canonical map. Because Map2 has expanded to overlap with at least a portion of the Canonical Map,the localization attempt will succeed. In some embodiments, the overlapbetween the local tracking map, Map 2, and the Canonical Map may berepresented by PCFs, persistent poses, key frames, or any other suitableintermediate or derivative construct.

Furthermore, the second XR device 12.2 has associated Content123 andContent456 to PCFs 1,2 and PCF 3 of Map 2. Content123 has X, Y, and Zcoordinates relative to PCF 1,2 of (1,0,0). Similarly, the X, Y, and Zcoordinates of Content456 relative to PCF 3 in Map 2 are (1,0,0).

FIGS. 46A and 46B illustrate a successful localization of Map 2 to thecanonical map. Localization may be based on matching features in one mapto the other. With an appropriate transformation, here involving bothtranslation and rotation of one map with respect to the other, theoverlapping area/volume/section of the maps 1410 represent the commonparts to Map 1 and the canonical map. Since Map 2 created PCFs 3 and 4,5before localizing, and the Canonical map created PCFs a and c before Map2 was created, different PCFs were created to represent the same volumein real space (e.g., in different maps).

As shown in FIG. 47 , the second XR device 12.2 expands Map 2 to includePCFs a-d from the Canonical Map. The inclusion of PCFs a-d representsthe localization of Map 2 to the Canonical Map. In some embodiments, theXR system may perform an optimization step to remove duplicate PCFs fromoverlapping areas, such as the PCFs in 1410, PCF 3 and PCF 4,5. AfterMap 2 localizes, the placement of virtual content, such as Content456and Content123 will be relative to the closest updated PCFs in theupdated Map 2. The virtual content appears in the same real-worldlocation relative to the user, despite the changed PCF attachment forthe content, and despite the updated PCFs for Map 2.

As shown in FIG. 48 , the second XR device 12.2 continues to expand Map2 as further PCFs (e.g., PCFs e, f, g, and h) are identified by thesecond XR device 12.2, for example as the user walks around the realworld. It can also be noted that Map 1 has not expanded in FIGS. 47 and48 .

Referring to FIG. 49 , the second XR device 12.2 uploads Map 2 to theserver 20. The server 20 stores Map 2 together with the canonical map.In some embodiments, Map 2 may upload to the server 20 when the sessionends for the second XR device 12.2.

The canonical map within the server 20 now includes PCF i which is notincluded in Map 1 on the first XR device 12.1. The canonical map on theserver 20 may have expanded to include PCF i when a third XR device (notshown) uploaded a map to the server 20 and such a map included PCF i.

In FIG. 50 , the server 20 merges Map 2 with the canonical map to form anew canonical map. The server 20 determines that PCFs a to d are commonto the canonical map and Map 2. The server expands the canonical map toinclude PCFs e to h and PCF 1,2 from Map 2 to form a new canonical map.The canonical maps on the first and second XR devices 12.1 and 12.2 arebased on Map 1 and are outdated.

In FIG. 51 , the server 20 transmits the new canonical map to the firstand second XR devices 12.1 and 12.2. In some embodiments, this may occurwhen the first XR device 12.1 and second device 12.2 try to localizeduring a different or new or subsequent session. The first and second XRdevices 12.1 and 12.2 proceed as described above to localize theirrespective local maps (Map 1 and Map 2 respectively) to the newcanonical map.

As shown in FIG. 52 , the head coordinate frame 96 or “headpose” isrelated to the PCFs in Map 2. In some embodiments, the origin of themap, Origin 2, is based on the headpose of second XR device 12.2 at thestart of the session. As PCFs are created during the session, the PCFsare placed relative to the world coordinate frame, Origin 2. The PCFs ofMap 2 serve as a persistent coordinate frames relative to a canonicalcoordinate frame, where the world coordinate frame may be a previoussession's world coordinate frame (e.g. Map 1's Origin 1 in FIG. 40 ).These coordinate frames are related by the same transformation used tolocalize Map 2 to the canonical map, as discussed above in connectionwith FIG. 46B.

The transformation from the world coordinate frame to the headcoordinate frame 96 has been previously discussed with reference to FIG.9 . The head coordinate frame 96 shown in FIG. 52 only has twoorthogonal axes that are in a particular coordinate position relative tothe PCFs of Map 2, and at particular angles relative to Map 2. It shouldhowever be understood that the head coordinate frame 96 is in athree-dimensional location relative to the PCFs of Map 2 and has threeorthogonal axes within three-dimensional space.

In FIG. 53 , the head coordinate frame 96 has moved relative to the PCFsof Map 2. The head coordinate frame 96 has moved because the second user14.2 has moved their head. The user can move their head in six degreesof freedom (6 dof). The head coordinate frame 96 can thus move in 6 dof,namely in three-dimensions from its previous location in FIG. 52 andabout three orthogonal axes relative to the PCFs of Map 2. The headcoordinate frame 96 is adjusted when the real object detection camera 44and inertial measurement unit 48 in FIG. 9 respectively detect realobjects and motion of the head unit 22. More information regardingheadpose tracking is disclosed in U.S. patent application Ser. No.16/221,065 entitled “Enhanced Pose Determination for Display Device” andis hereby incorporated by reference in its entirety.

FIG. 54 shows that sound may be associated with one or more PCFs. A usermay, for example, wear headphones or earphones with stereoscopic sound.The location of sound through headphones can be simulated usingconventional techniques. The location of sound may be located in astationary position so that, when the user rotates their head to theleft, the location of sound rotates to the right so that the userperceives the sound coming from the same location in the real world. Inthe present example, location of sound is represented by Sound123 andSound456. For purposes of discussion, FIG. 54 is similar to FIG. 48 inits analysis. When the first and second users 14.1 and 14.2 are locatedin the same room at the same or different times, they perceive Sound123and Sound456 coming from the same locations within the real world.

FIGS. 55 and 56 illustrate a further implementation of the technologydescribed above. The first user 14.1 has initiated a first session asdescribed with reference to FIG. 8 . As shown in FIG. 55 , the firstuser 14.1 has terminated the first session as indicated by the phantomlines. At the end of the first session, the first XR device 12.1uploaded Map 1 to the server 20. The first user 14.1 has now initiated asecond session at a later time than the first session. The first XRdevice 12.1 does not download Map 1 from the server 20 because Map 1 isalready stored on the first XR device 12.1. If Map 1 is lost, then thefirst XR device 12.1 downloads Map 1 from the server 20. The first XRdevice 12.1 then proceeds to build PCFs for Map 2, localizes to Map 1,and further develops a canonical map as described above. Map 2 of thefirst XR device 12.1 is then used for relating local content, a headcoordinate frame, local sound, etc. as described above.

Referring to FIGS. 57 and 58 , it may also be possible that more thanone user interacts with the server in the same session. In the presentexample, the first user 14.1 and the second user 14.2 are joined by athird user 14.3 with a third XR device 12.3. Each XR device 12.1, 12.2,and 12.3 begins to generate its own map, namely Map 1, Map 2, and Map 3,respectively. As the XR devices 12.1, 12.2, and 12.3 continue to developMaps 1, 2, and 3, the maps are incrementally uploaded to the server 20.The server 20 merges Maps 1, 2, and 3 to form a canonical map. Thecanonical map is then transmitted from the server 20 to each one of theXR devices 12.1, 12.2 and 12.3.

FIG. 59 illustrates aspects of a viewing method to recover and/or resetheadpose, according to some embodiments. In the illustrated example, atAct 1400, the viewing device is powered on. At Act 1410, in response tobeing powered on, a new session is initiated. In some embodiments, a newsession may include establishing headpose. One or more capture deviceson a head-mounted frame secured to a head of a user capture surfaces ofan environment by first capturing images of the environment and thendetermining the surfaces from the images. In some embodiments, surfacedata may be combined with a data from a gravitational sensor toestablish headpose. Other suitable methods of establishing headpose maybe used.

At Act 1420, a processor of the viewing device enters a routine fortracking of headpose. The capture devices continue to capture surfacesof the environment as the user moves their head to determine anorientation of the head-mounted frame relative to the surfaces.

At Act 1430, the processor determines whether headpose has been lost.Headpose may become lost due to “edge” cases, such as too manyreflective surfaces, low light, blank walls, being outdoor, etc. thatmay result in low feature acquisition, or because of dynamic cases suchas a crowd that moves and forms part of the map. The routine at 1430allows for a certain amount of time, for example 10 seconds, to pass toallow enough time to determine whether headpose has been lost. Ifheadpose has not been lost, then the processor returns to 1420 and againenters tracking of headpose.

If headpose has been lost at Act 1430, the processor enters a routine at1440 to recover headpose. If headpose is lost due to low light, then amessage such as the following message is displayed to the user through adisplay of the viewing device:

THE SYSTEM IS DETECTING A LOW LIGHT CONDITION. PLEASE MOVE TO AN AREAWHERE THERE IS MORE LIGHT.

The system will continue to monitor whether there is sufficient lightavailable and whether headpose can be recovered. The system mayalternatively determine that low texture of surfaces is causing headposeto be lost, in which case the user is given the following prompt in thedisplay as a suggestion to improve capturing of surfaces:

THE SYSTEM CANNOT DETECT ENOUGH SURFACES WITH FINE TEXTURES. PLEASE MOVETO AN AREA WHERE THE SURFACES ARE LESS ROUGH IN TEXTURE AND MORE REFINEDIN TEXTURE.

At Act 1450, the processor enters a routine to determine whetherheadpose recovery has failed. If headpose recovery has not failed (i.e.headpose recovery has succeeded), then the processor returns to Act 1420by again entering tracking of headpose. If headpose recovery has failed,the processor returns to Act 1410 to establish a new session. As part ofthe new session, all cached data is invalidated, whereafter headpose isestablished anew. Any suitable method of head tracking may be used incombination with the process described in FIG. 59 . U.S. patentapplication Ser. No. 16/221,065 describes head tracking and is herebyincorporated by reference in its entirety.

Remote Localization

Various embodiments may utilize remote resources to facilitatepersistent and consistent cross reality experiences between individualand/or groups of users. The inventors have recognized and appreciatedthat the benefits of operation of an XR device with canonical maps asdescribed herein can be achieved without downloading a set of canonicalmaps, such as is illustrated in FIG. 30 . The benefit, for example, maybe achieved by sending feature and pose information to a remote servicethat maintains a set of canonical maps. A device seeking to use acanonical map to position virtual content in locations specifiedrelative to the canonical map may receive from the remote service one ormore transformations between the features and the canonical maps. Thosetransformations may be used on the device, which maintains informationabout the positions of those features in the physical world, to positionvirtual content in locations specified with respect to canonical map orto otherwise identify locations in the physical world that are specifiedwith respect to the canonical map.

In some embodiments, spatial information is captured by an XR device andcommunicated to a remote service, such as a cloud based service, whichuses the spatial information to localize the XR device to a canonicalmap used by applications or other components of an XR system to specifythe location of virtual content with respect to the physical world. Oncelocalized, transforms that link a tracking map maintained by the deviceto the canonical map can be communicated to the device. The transformsmay be used, in conjunction with the tracking map, to determine aposition in which to render virtual content specified with respect tothe canonical map, or otherwise identify locations in the physical worldthat are specified with respect to the canonical map.

The inventors have realized that the data needed to be exchanged betweena device and a remote localization service can be quite small relativeto communicating map data, as might occur when a device communicates atracking map to a remote service and receives from that service a set ofcanonical maps for device based localization). In some embodiments,performing localization functions on cloud resources requires only smallamount of information to be transmitted from the device to the remoteservice. It is not a requirement, for example, that a full tracking mapbe communicated to the remote service to perform localization. In someembodiments, features and pose information, such as might be stored inconnection with a persistent pose, as described above, might betransmitted to the remote server. In embodiments in which features arerepresented by descriptors, as described above, the information uploadedmay be even smaller.

The results returned to the device from the localization service may beone or more transformations that relate the uploaded features toportions of a matching canonical map. Those transformations may be usedwithin the XR system, in conjunction with its tracking map, foridentifying locations of virtual content or otherwise identifyinglocations in the physical world. In embodiments in which persistentspatial information, such as PCFs as described above, are used tospecify locations with respect to a canonical map, the localizationservice may download to the device transformations between the featuresand one or more PCFs after a successful localization.

As a result, network bandwidth consumed by communications between an XRdevice and a remote service for performing localization may be low. Thesystem may therefore support frequent localization, enabling each deviceinteracting with the system to quickly obtain information forpositioning virtual content or performing other location-basedfunctions. As a device moves within the physical environment, it mayrepeat requests for updated localization information. Additionally, adevice may frequently obtain updates to the localization information,such as when the canonical maps change, such as through merging ofadditional tracking maps to expand the map or increase their accuracy.

Further, uploading features and downloading transformations can enhanceprivacy in an XR system that shares map information among multiple usersby increasing the difficulty of obtaining maps by spoofing. Anunauthorized user, for example, may be thwarted from obtaining a mapfrom the system by sending a fake request for a canonical maprepresenting a portion of the physical world in which that unauthorizeduser is not located. An unauthorized user would be unlikely to haveaccess to the features in the region of the physical world for which itis requesting map information if not physically present in that region.In embodiments in which feature information is formatted as featuredescriptions, the difficulty in spoofing feature information in arequest for map information would be compounded. Further, when thesystem returns a transformation intended to be applied to a tracking mapof a device operating in the region about which location information isrequested, the information returned by the system is likely to be oflittle or no use to an imposter.

According to one embodiment, a localization service is implemented as acloud based micro-service. In some examples, implementing a cloud-basedlocalization service can help save device compute resources and mayenable computations required for localization to be performed with verylow latency. Those operations can be supported by nearly infinitecompute power or other computing resources available by provisioningadditional cloud resources, ensuring scalability of the XR system tosupport numerous devices. In one example, many canonical maps can bemaintained in memory for nearly instant access, or alternatively storedin high availability devices reducing system latency.

Further, performing localization for multiple devices in a cloud servicemay enable refinements to the process. Localization telemetry andstatistics can provide information on which canonical maps to have inactive memory and/or high availability storage. Statistics for multipledevices may be used, for example, to identify most frequently accessedcanonical maps.

Additional accuracy may also be achieved as a result of processing in acloud environment or other remote environment with substantialprocessing resources relative to a remote device. For example,localization can be made on higher density canonical maps in the cloudrelative to processing performed on local devices. Maps may be stored inthe cloud, for example, with more PCFs or a greater density of featuredescriptors per PCF, increasing the accuracy of a match between a set offeatures from a device and a canonical map.

FIG. 61 is a schematic diagram of an XR system 6100. The user devicesthat display cross reality content during user sessions can come in avariety of forms. For example, a user device can be a wearable XR device(e.g., 6102) or a handheld mobile device (e.g., 6104). As discussedabove, these devices can be configured with software, such asapplications or other components, and/or hardwired to generate localposition information (e.g., a tracking map) that can be used to rendervirtual content on their respective displays.

Virtual content positioning information may be specified with respect toglobal location information, which may be formatted as a canonical mapcontaining one or more PCFs, for example. According to some embodiments,for example the embodiment shown in FIG. 61 , the system 6100 isconfigured with cloud based services that support the functioning anddisplay of the virtual content on the user device.

In one example, localization functions are provided as a cloud-basedservice 6106, which may be a micro-service. Cloud-based service 6106 maybe implemented on any of multiple computing devices, from whichcomputing resources may be allocated to one or more services executingin the cloud. Those computing devices may be interconnected with eachother and accessibly to devices, such as a wearable XR device 6102 andhand held device 6104. Such connections may be provided over one or morenetworks.

In some embodiments, the cloud-based service 6106 is configured toaccept descriptor information from respective user devices and“localize” the device to a matching canonical map or maps. For example,the cloud-based localization service matches descriptor informationreceived to descriptor information for respective canonical map(s). Thecanonical maps may be created using techniques as described above thatcreate canonical maps by merging maps provided by one or more devicesthat have image sensors or other sensors that acquire information abouta physical world. However, it is not a requirement that the canonicalmaps be created by the devices that access them, as such maps may becreated by a map developer, for example, who may publish the maps bymaking them available to localization service 6106.

According to some embodiments, the cloud service handles canonical mapidentification, and may include operations to filter a repository ofcanonical maps to a set of potential matches. Filtering may be performedas illustrated in FIG. 29 , or by using any subset of the filtercriteria and other filter criteria instead of or in addition to thefilter criteria shown in FIG. 29 . In one embodiment, geographic datacan be used to limit a search for matching canonical map to mapsrepresenting areas proximate to the device requesting localization. Forexample, area attributes such as Wi-Fi signal data, Wi-Fi fingerprintinformation, GPS data, and/or other device location information can beused as a coarse filter on stored canonical maps, and thereby limitanalysis of descriptors to canonical maps known or likely to be inproximity to the user device. Similarly, location history of each devicemay be maintained by the cloud service such that canonical maps in thevicinity of the device's last location are preferentially searched. Insome examples, filtering can include the functions discussed above withrespect to FIGS. 31B, 32, 33, and 34 .

FIG. 62 is an example process flow that can be executed by a device touse a cloud-based service to localize the device's position withcanonical map(s) and receive transform information specifying one ormore transformations between the device local coordinate system and thecoordinate system of a canonical map. Various embodiments and examplesmay describe the one or more transforms as specifying transforms from afirst coordinate frame to a second coordinate frame. Other embodimentsinclude transforms from the second coordinate frame to the firstcoordinate frame. In yet other embodiments, the transforms enabletransition from one coordinate frame to another, the resultingcoordinate frames depend only on the desired coordinate frame output(including, for example, the coordinate frame in which to displaycontent). In yet further embodiments, the coordinate system transformsmay enable determination of a first coordinate frame from the secondcoordinate frame and the second coordinate frame from the firstcoordinate frame.

According to some embodiments, information reflecting a transform foreach persistent pose defined with respect to the canonical map can becommunicated to device.

According to one embodiment, process 6200 can begin at 6202 with a newsession. Starting new session on the device may initiate capture ofimage information to build a tracking map for the device. Additionally,the device may send a message, registering with a server of alocalization service, prompting the server to create a session for thatdevice.

In some embodiments, starting a new session on a device optionally mayinclude sending adjustment data from the device to the localizationservice. The localization service returns to the device one or moretransforms computed based on the set of features and associated poses.If the poses of the features are adjusted based on device-specificinformation before computation of the transformation and/or thetransformations are adjusted based on device-specific information aftercomputation of the transformation, rather than perform thosecomputations on the device, the device specific information might besent to the localization service, such that the localization service mayapply the adjustments. As a specific example, sending device-specificadjustment information may include capturing calibration data forsensors and/or displays. The calibration data may be used, for example,to adjust the locations of feature points relative to a measuredlocation. Alternatively or additionally, the calibration data may beused to adjust the locations at which the display is commanded to rendervirtual content so as to appear accurately positioned for thatparticular device. This calibration data may be derived, for example,from multiple images of the same scene taken with sensors on the device.The locations of features detected in those images may be expressed as afunction of sensor location, such that multiple images yield a set ofequations that may be solved for the sensor location. The computedsensor location may be compared to a nominal position, and thecalibration data may be derived from any differences. In someembodiments, intrinsic information about the construction of the devicemay also enable calibration data to be computed for the display, in someembodiments.

In embodiments in which calibration data is generated for the sensorsand/or display, the calibration data may be applied at any point in themeasurement or display process. In some embodiments, the calibrationdata may be sent to the localization server, which may store thecalibration data in a data structure established for each device thathas registered with the localization server and is therefore in asession with the server. The localization server may apply thecalibration data to any transformations computed as part of alocalization process for the device supplying that calibration data. Thecomputational burden of using the calibration data for greater accuracyof sensed and/or displayed information is thus borne by the calibrationservice, providing a further mechanism to reduce processing burden onthe devices.

Once the new session is established, process 6200 may continue at 6204with capture of new frames of the device's environment. Each frame canbe processed to generate descriptors (including for example, DSF valuesdiscussed above) for the captured frame at 6206. These values may becomputed using some or all of the techniques described above, includingtechniques as discussed above with respect to FIGS. 14, 22 and 23 . Asdiscussed, the descriptors may be computed as a mapping of the featurepoints or, in some embodiments a mapping of a patch of an image around afeature point, to a descriptor. The descriptor may have a value thatenables efficient matching between newly acquired frames/images andstored maps. Moreover, the number of features extracted from an imagemay be limited to a maximum number of features points per image, such as200 feature points per image. The feature points may be selected torepresent interest points, as described above. Accordingly, acts 6204and 6206 may be performed as part of a device process of forming atracking map or otherwise periodically collecting images of the physicalworld around the device, or may be, but need not be, separatelyperformed for localization.

Feature extraction at 6206 may include appending pose information to theextracted features at 6206. The pose information may be a pose in thedevice's local coordinate system. In some embodiments, the pose may berelative to a reference point in the tracking map, such as a persistentpose, as discussed above. Alternatively or additionally, the pose may berelative to the origin of a tracking map of the device. Such anembodiment may enable the localization service as described herein toprovide localization services for a wide range of devices, even if theydo not utilize persistent poses. Regardless, pose information may beappended to each feature or each set of features, such that thelocalization service may use the pose information for computing atransformation that can be returned to the device upon matching thefeatures to features in a stored map.

The process 6200 may continue to decision block 6207 where a decision ismade whether to request localization. One or more criteria may beapplied to determine whether to request localization. The criteria mayinclude passage of time, such that a device may request localizationafter some threshold amount of time. For example, if localization hasnot been attempted within a threshold amount of time, the process maycontinue from decision block 6207 to act 6208 where localization isrequested from the cloud. That threshold amount of time may be betweenten and thirty seconds, such as twenty-five seconds, for example.Alternatively or additionally, localization may be triggered by motionof a device. A device executing the process 6200 may track its motionusing an IMU and/or its tracking map, and initiate localization upondetection motion exceeding a threshold distance from the location wherethe device last requested localization. The threshold distance may bebetween one and ten meters, such as between three and five meters, forexample. As yet a further alternative, localization may be triggered inresponse to an event, such as when a device creates a new persistentpose or the current persistent pose for the device changes, as describedabove.

In some embodiments, decision block 6207 may be implemented such thatthe thresholds for triggering localization may be establisheddynamically. For example, in environments in which features are largelyuniform such that there may be a low confidence in matching a set ofextracted features to features of a stored map, localization may berequested more frequently to increase the chances that at least oneattempt at localization will succeed. In such a scenario, the thresholdsapplied at decision block 6207 may be decreased. Similarly, in anenvironment in which there are relatively few features, the thresholdsapplied at decision block 6207 may be decreased so as to increase thefrequency of localization attempts.

Regardless of how the localization is triggered, when triggered, theprocess 6200 may proceed to act 6208 where the device sends a request tothe localization service, including data used by the localizationservice to perform localization. In some embodiments, data from multipleimage frames may be provided for a localization attempt. Thelocalization service, for example, may not deem localization successfulunless features in multiple image frames yield consistent localizationresults. In some embodiments, process 6200 may include saving featuredescriptors and appended pose information into a buffer. The buffer may,for example, be a circular buffer, storing sets of features extractedfrom the most recently captured frames. Accordingly, the localizationrequest may be sent with a number of sets of features accumulated in thebuffer. In some settings, a buffer size is implemented to accumulate anumber of sets of data that will be more likely to yield successfullocalization. In some embodiments, a buffer size may be set toaccumulate features from two, three, four, five, six, seven, eight,nine, or ten frames, for example). Optionally, the buffer size can havea baseline setting which can be increased responsive to localizationfailures. In some examples, increasing the buffer size and correspondingnumber of sets of features transmitted reduces the likelihood thatsubsequent localization functions fail to return a result.

Regardless of how the buffer size is set, the device may transfer thecontents of the buffer to the localization service as part of alocalization request. Other information may be transmitted inconjunction with the feature points and appended pose information. Forexample, in some embodiments, geographic information may be transmitted.The geographic information may include, for example, GPS coordinates ora wireless signature associated with the devices tracking map or currentpersistent pose.

In response to the request sent at 6208, a cloud localization servicemay analyze the feature descriptors to localize the device into acanonical map or other persistent map maintained by the service. Forexample, the descriptors are matched to a set of features in a map towhich the device is localized. The cloud based localization service mayperform localization as described above with respect to device basedlocalization (e.g., can rely on any of the functions discussed above forlocalization including, map ranking, map filtering, location estimation,filtered map selection, examples in FIGS. 44-46 , and/or discussed withrespect to a localization module, PCF and/or PP identification andmatching etc.). However, instead of communicating identified canonicalmaps to a device (e.g., in device localization), the cloud-basedlocalization service may proceed to generate transforms based on therelative orientation of feature sets sent from the device and thematching features of the canonical maps. The localization service mayreturn these transforms to the device, which may be received at block6210.

In some embodiments, the canonical maps maintained by the localizationservice may employ PCFs, as described above. In such embodiments, thefeature points of the canonical maps that match the feature points sentfrom the device may have positions specified with respect to one or morePCFs. Accordingly, the localization service may identify one or morecanonical maps and may compute a transformation between the coordinateframe represented in the poses sent with the request for localizationand the one or more PCFs. In some embodiments, identification of the oneor more canonical maps is assisted by filtering potential maps based ongeographic data for a respective device. For example, once filtered to acandidate set (e.g., by GPS coordinate, among other options) thecandidate set of canonical maps can be analyzed in detail to determinematching feature points or PCFs as described above.

The data returned to the requesting device at act 6210 may be formattedas a table of persistent pose transforms. The table can be accompaniedby one or more canonical map identifiers, indicating the canonical mapsto which the device was localized by the localization service. However,it should be appreciated that the localization information may beformatted in other ways, including as a list of transforms, withassociated PCF and/or canonical map identifiers.

Regardless of how the transforms are formatted, at act 6212 the devicemay use these transforms to compute the location at which to rendervirtual content for which a location has been specified by anapplication or other component of the XR system relative to any of thePCFs. This information may alternatively or additionally be used on thedevice to perform any location based operation in which a location isspecified based on the PCFs.

In some scenarios, the localization service may be unable to matchfeatures sent from a device to any stored canonical map or may not beable to match a sufficient number of the sets of features communicatedwith the request for the localization service to deem a successfullocalization occurred. In such a scenario, rather than returningtransformations to the device as described above in connection with act6210, the localization service may indicate to the device thatlocalization failed. In such a scenario, the process 6200 may branch atdecision block 6209 to act 6230, where the device may take one or moreactions for failure processing. These actions may include increasing thesize of the buffer holding feature sets sent for localization. Forexample, if the localization service does not deem a successfullocalization unless three sets of features match, the buffer size may beincreased from five to six, increasing the chances that three of thetransmitted sets of features can be matched to a canonical mapmaintained by the localization service.

Alternatively or additionally, failure processing may include adjustingan operating parameter of the device to trigger more frequentlocalization attempts. The threshold time between localization attemptsand/or the threshold distance may be decreased, for example. As anotherexample, the number of feature points in each set of features may beincreased. A match between a set of features and features stored withina canonical map may be deemed to occur when a sufficient number offeatures in the set sent from the device match features of the map.Increasing the number of features sent may increase the chances of amatch. As a specific example, the initial feature set size may be 50,which may be increased to 100, 150, and then 200, on each successivelocalization failure. Upon successful match, the set size may then bereturned to its initial value.

Failure processing may also include obtaining localization informationother than from the localization service. According to some embodiments,the user device can be configured to cache canonical maps. Cached mapspermit devices to access and display content where the cloud isunavailable. For example, cached canonical maps permit device basedlocalization in the event of communication failure or otherunavailability.

According to various embodiments, FIG. 62 describes a high level flowfor a device initiating cloud based localization. In other embodiments,various ones or more of the illustrated steps can be combined, omitted,or invoke other processes to accomplish localization and ultimatelyvisualization of virtual content in a view of a respective device.

Further, it should be appreciated that, though the process 6200 showsthe device determining whether to initiate localization at decisionblock 6207, the trigger for initiating localization may come fromoutside the device, including from the localization service. Thelocalization service, for example, may maintain information about eachof the devices that is in a session with it. That information, forexample, may include an identifier of a canonical map to which eachdevice most recently localized. The localization service, or othercomponents of the XR system, may update canonical maps, including usingtechniques as described above in connection with FIG. 26 . When acanonical map is updated, the localization service may send anotification to each device that most recently localized to that map.That notification may serve as a trigger for the device to requestlocalization and/or may include updated transformations, recomputedusing the most recently sent sets of features from the device.

FIGS. 63A, B, and C are an example process flow showing operations andcommunication between a device and cloud services. Shown at blocks 6350,6352 6354, and 6456 are example architecture and separation betweencomponents participating in the cloud based localization process. Forexample, the modules, components, and/or software that are configured tohandle perception on the user device are shown at 6350 (e.g., 660, FIG.6A). Device functionality for persisted world operations are shown at6352 (including, for example, as described above and with respect topersisted world module (e.g., 662, FIG. 6A)). In other embodiments, theseparation between 6350 and 6352 is not needed and the communicationshown can be between processes executing on the device.

Similarly, shown at block 6354 is a cloud process configured to handlefunctionality associated with passable world/passable world modeling(e.g., 802, 812, FIG. 26 ). Shown at block 6356 is a cloud processconfigured to handle functionality associated with localizing a device,based on information sent from a device, to one or more maps of arepository of stored canonical maps.

In the illustrated embodiment, process 6300 begins at 6302 when a newsession starts. At 6304 sensor calibration data is obtained. Thecalibration data obtained can be dependent on the device represented at6350 (e.g., number of cameras, sensors, positioning devices, etc.). Oncethe sensor calibration is obtained for the device, the calibrations canbe cached at 6306. If device operation resulted in a change in frequencyparameters (e.g., collection frequency, sampling frequency, matchingfrequency, among other options) the frequency parameters are reset tobaseline at 6308.

Once the new session functions are complete (e.g., calibration, steps6302-6306) process 6300 can continue with capture of a new frame 6312.Features and their corresponding descriptors are extracted from theframe at 6314. In some examples, descriptors can comprise DSF's, asdiscussed above. According to some embodiments, the descriptors can havespatial information attached to them to facilitate subsequent processing(e.g., transform generation). Pose information (e.g., information,specified relative to the device's tracking map for locating thefeatures in the physical world as discussed above) generated on thedevice can be appended to the extracted descriptors at 6316.

At 6318, the descriptor and pose information is added to a buffer. Newframe capture and addition to the buffer shown in steps 6312-6318 isexecuted in a loop until a buffer size threshold is exceeded at 6319.Responsive to a determination that the buffer size has been met, alocalization request is communicated from the device to the cloud at6320. According to some embodiments, the request can be handled by apassable world service instantiated in the cloud (e.g. 6354). In furtherembodiments, functional operations for identifying candidate canonicalmaps can be segregated from operations for actual matching (e.g., shownas blocks 6354 and 6356). In one embodiment, a cloud service for mapfiltering and/or map ranking can be executed at 6354 and process thereceived localization request from 6320. According to one embodiment,the map ranking operations are configured to determine a set ofcandidate maps at 6322 that are likely to include a device's location.

In one example, the map ranking function includes operations to identifycandidate canonical maps based on geographic attributes or otherlocation data (e.g., observed or inferred location information). Forexample, other location data can include Wi-Fi signatures or GPSinformation.

According to other embodiments, location data can be captured during across reality session with the device and user. Process 6300 can includeadditional operations to populate a location for a given device and/orsession (not shown). For example, the location data may be stored asdevice area attribute values and the attribute values used to selectcandidate canonical maps proximate to the device's location.

Any one or more of the location options can be used to filter sets ofcanonical maps to those likely to represent an area including thelocation of a user device. In some embodiments, the canonical maps maycover relatively large regions of the physical world. The canonical mapsmay be segmented into areas such that selection of a map may entailselection of a map area. A map area, for example may be on the order oftens of meters squared. Thus, the filtered set of canonical maps may bea set of areas of the maps.

According to some embodiments, a localization snapshot can be built fromthe candidate canonical maps, posed features, and sensor calibrationdata. For example, an array of candidate canonical maps, posed features,and sensor calibration information can be sent with a request todetermine specific matching canonical maps. Matching to a canonical mapcan be executed based on descriptors received from a device and storedPCF data associated with the canonical maps.

In some embodiments, a set of features from the device is compared tosets of features stored as part of the canonical map. The comparison maybe based on the feature descriptors and/or pose. For example, acandidate set of features of a canonical map may be selected based onthe number of features in the candidate set that have descriptorssimilar enough to the descriptors of the feature set from the devicethat they could be the same feature. The candidate set, for example, maybe features derived from an image frame used in forming the canonicalmap.

In some embodiments, if the number of similar features exceeds athreshold, further processing may be performed on the candidate set offeatures. Further processing may determine the degree to which the setof posed features from the device can be aligned with the features ofthe candidate set. The set of features from the canonical map, like thefeatures from the device, may be posed.

In some embodiments, features are formatted as a highly dimensionalembedding (e.g., DSF, etc.) and may be compared using a nearest neighborsearch. In one example, the system is configured (e.g., by executingprocess 6200 and/or 6300) to find the top two nearest neighbors usingEuclidian distance, and may execute a ratio test. If the closestneighbor is much closer than the second closest neighbor, the systemconsiders the closest neighbor to be a match. “Much closer” in thiscontext may be determined, for example, by the ratio of Euclideandistance relative to the second nearest neighbor is more than athreshold times the Euclidean distance relative to the nearest neighbor.Once a feature from the device is considered to be a “match” to afeature in canonical map, the system may be configured to use the poseof the matching features to compute a relative transformation. Thetransformation developed from the pose information may be used toindicate the transformation required to localize the device to thecanonical map.

The number of inliers may serve as an indication of the quality of thematch. For example, in the case of DSF matching, the number of inliersreflects the number of features that were matched between receiveddescriptor information and stored/canonical maps. In furtherembodiments, inliers may be determined in this embodiment by countingthe number of features in each set that “match.”

An indication of the quality of a match may alternatively oradditionally be determined in other ways. In some embodiments, forexample, when a transformation is computed to localize a map from adevice, which may contain multiple features, to a canonical map, basedon relative pose of matching features, statistics of the transformationcomputed for each of multiple matching features may serve as qualityindication. A large variance, for example, may indicate a poor qualityof match. Alternatively or additionally, the system may compute, for adetermined transformation, a mean error between features with matchingdescriptors. The mean error may be computed for the transformation,reflecting the degree of positional mismatch. A mean squared error is aspecific example of an error metric. Regardless of the specific errormetric, if the error is below a threshold, the transformation may bedetermined to be usable for the features received from the device, andthe computed transformation is used for localizing the device.Alternatively or additionally, the number of inliers may also be used indetermining whether there is a map that matches a device's positionalinformation and/or descriptors received from a device.

As noted above, in some embodiments, a device may send multiple sets offeatures for localization. Localization may be deemed successful when atleast a threshold number of sets of features match, with an error belowa threshold, and/or a number of inliers above a threshold, a set offeatures from the canonical map. That threshold number, for example, maybe three sets of features. However, it should be appreciated that thethreshold used for determining whether a sufficient number of sets offeature have suitable values may be determined empirically or in othersuitable ways. Likewise, other thresholds or parameters of the matchingprocess, such as degree of similarity between feature descriptors to bedeemed matching, the number of inliers for selection of a candidate setof features, and/or the magnitude of the mismatch error, may similarlybe determined empirically or in other suitable ways.

Once a match is determined, a set of persistent map features associatedwith the matched canonical map or maps is identified. In embodiments inwhich the matching is based on areas of maps, the persistent mapfeatures may be the map features in the matching areas. The persistentmap features may be persistent poses or PCFs as described above. In theexample of FIG. 63 , the persistent map features are persistent poses.

Regardless of the format of the persistent map features, each persistentmap feature may have a predetermined orientation relative to thecanonical map in which it is a part. This relative orientation may beapplied to the transformation computed to align the set of features fromthe device with the set of features from the canonical map to determinea transformation between the set of features from the device and thepersistent map feature. Any adjustments, such as might be derived fromcalibration data, may then be applied to this computed transformation.The resulting transformation may be the transformation between the localcoordinate frame of the device and the persistent map feature. Thiscomputation may be performed for each persistent map feature of amatching map area, and the results may be stored in a table, denoted asthe persistent_pose_table in 6326.

In one example, block 6326 returns a table of persistent posetransforms, canonical map identifiers, and number of inliers. Accordingto some embodiments, the canonical map ID is an identifier for uniquelyidentifying a canonical map and a version of the canonical map (or areaof a map, in embodiments in which localization is based on map areas).

In various embodiments, the computed localization data can be used topopulate localization statistics and telemetry maintained by thelocalization service at 6328. This information may be stored for eachdevice, and may be updated for each localization attempt, and may becleared when the device's session ends. For example, which maps werematched by a device can be used to refine map ranking operations. Forexample, maps covering the same area to which the device previouslymatched may be prioritized in the ranking. Likewise, maps coveringadjacent areas may be give higher priority over more remote areas.Further, the adjacent maps might be prioritized based on a detectedtrajectory of the device over time, with map areas in the direction ofmotion being given higher priority over other map areas. Thelocalization service may use this information, for example, upon asubsequent localization request from the device to limit the maps or mapareas searched for candidate sets of features in the stored canonicalmaps. If a match, with low error metrics and/or a large number orpercentage of inliers, is identified in this limited area, processing ofmaps outside the area may be avoided.

Process 6300 can continue with communication of information from thecloud (e.g., 6354) to the user device (e.g., 6352). According to oneembodiment, a persistent pose table and canonical map identifiers arecommunicated to the user device at 6330. In one example, the persistentpose table can be constructed of elements including at least a stringidentifying a persistent pose ID and a transform linking the device'stracking map and the persistent pose. In embodiments in which thepersistent map features are PCFs the table may, instead, indicatetransformations to the PCFs of the matching maps.

If localization fails at 6336, process 6300 continues by adjustingparameters that may increase the amount of data sent from a device tothe localization service to increases the chances that localization willsucceed. Failure, for example, may be indicated when no sets of featuresin the canonical map can be found with more than a threshold number ofsimilar descriptors or when the error metric associated with alltransformed sets of candidate features is above a threshold. As anexample of a parameter that may be adjusted, the size constraint for thedescriptor buffer may be increased (of 6319). For example, where thedescriptor buffer size is five, localization failure can trigger anincrease to at least six sets of features, extracted from at least siximage frames. In some embodiments, process 6300 can include a descriptorbuffer increment value. In one example, the increment value can be usedto control the rate of increase in the buffer size, for example,responsive to localization failures. Other parameters, such asparameters controlling the rate of localization requests, may be changedupon a failure to find matching canonical maps.

In some embodiments, execution of 6300 can generate an error conditionat 6340, which includes execution where the localization request failsto work, rather than return a no match result. An error, for example,may occur as a result of a network error making the storage holding adatabase of canonical maps unavailable to a server executing thelocalization service or a received request for localization servicescontaining incorrectly formatted information. In the event of an errorcondition, in this example, the process 6300 schedules a retry of therequest at 6342.

When a localization request is successful, any parameters adjusted inresponse to a failure may be reset. At 6332, process 6300 can continuewith an operation to reset frequency parameters to any default orbaseline. In some embodiments 6332 is executed regardless of any changesthus ensuring baseline frequency is always established.

The received information can be used by the device at 6334 to update acache localization snapshot. According to various embodiments, therespective transforms, canonical maps identifiers, and otherlocalization data can be stored by the device and used to relatelocations specified with respect to the canonical maps, or persistentmap features of them such as persistent poses or PCFs to locationsdetermined by the device with respect to its local coordinate frame,such as might be determined from its tracking map.

Various embodiments of processes for localization in the cloud canimplement any one or more of the preceding steps and be based on thepreceding architecture. Other embodiments may combine various ones ormore of the preceding steps, execute steps simultaneously, in parallel,or in another order.

According to some embodiments, localization services in the cloud in thecontext of cross reality experiences can include additionalfunctionality. For example, canonical map caching may be executed toresolve issues with connectivity. In some embodiments, the device mayperiodically download and cache canonical maps to which it haslocalized. If the localization services in the cloud are unavailable,the device may run localizations itself (e.g., as discussedabove—including with respect to FIG. 26 ). In other embodiments, thetransformations returned from localization requests can be chainedtogether and applied in subsequent sessions. For example, a device maycache a train of transformations and use the sequence of transformationsto establish localization.

Various embodiments of the system can use the results of localizationoperations to update transformation information. For example, thelocalization service and/or a device can be configured to maintain stateinformation on a tracking map to canonical map transformations. Thereceived transformations can be averaged over time. According to oneembodiment, the averaging operations can be limited to occur after athreshold number of localizations are successful (e.g., three, four,five, or more times). In further embodiments, other state informationcan be tracked in the cloud, for example, by a passable world module. Inone example, state information can include a device identifier, trackingmap ID, canonical map reference (e.g., version and ID), and thecanonical map to tracking map transform. In some examples, the stateinformation can be used by the system to continuously update and getmore accurate canonical map to tracking map transforms with everyexecution of the cloud-based localization functions.

Additional enhancements to cloud-based localization can includecommunicating to devices outliers in the sets of features that did notmatch features in the canonical maps. The device may use thisinformation, for example, to improve its tracking map, such as byremoving the outliers from the sets of features used to build itstracking map. Alternatively or additionally, the information from thelocalization service may enable the device to limit bundle adjustmentsfor its tracking map to computing adjustments based on inlier featuresor to otherwise impose constraints on the bundle adjustment process.

According to another embodiment, various sub-processes or additionaloperations can be used in conjunction and/or as alternatives to theprocesses and/or steps discussed for cloud based localization. Forexample, candidate map identification may include accessing canonicalmaps based on area identifiers and/or area attributes stored withrespective maps.

Deep Correspondences

Described herein are methods and apparatus for efficiently andaccurately finding matching sets of feature points, such as may occur inlocalizing XR devices in large scale environments in real-time.Accordingly, matching sets of features as part of localization isdescribed herein to illustrate techniques that may lead to fast andaccurate matches. Some or all of these techniques may be applied whenmatching sets of features in other contexts, such as when searching fora match between a portion of a tracking map and a canonical map as partof a map merge process.

Localizing XR devices may require making comparisons to find a matchbetween a set of 2D features from one or more images captured by the XRdevices and a set of feature points, which may be 3D map points in astored canonical map. Maps for large scale environments may include alarge number of 3D map points.

Some of the 3D map points may be captured at different times during theday or at different seasons compared with the 2D image features. Thedifferent dimensionality, different lighting condition and otherconditions makes it more difficult to accurately find matching sets offeatures. Accurate localization, for example, in large and very largescale environments, may require a larger number of sets of 2D featuresto be compared to provide an accurate localization result. As a result,localizing XR devices in large and very large scale environments takesmore time and consumes more computing power, causing delays indisplaying virtual contents and affecting the realisticity of XRexperiences.

The inventors have recognized and appreciated methods and apparatus thatlocalize XR devices in large and very large scale environments withreduced time and improved accuracy with a service that searches formatching sets of features using subsets of features with matchingdescriptors. The system may include a component that assess thelikelihood that including a pair of features with matching descriptionsin a subset will lead to finding matching sets of features.

In some embodiments, a localization service guided in the selection ofsubsets of features with matching features by the component may providea localization result in real time, such as no more than tenmilliseconds, five milliseconds, or two milliseconds in someembodiments. In some embodiments, the localization service guided by thecomponent may reduce the runtime to respond to a localization request byabout ten times, for example, from 25 ms to 3 ms with one hundredcorresponding features, with similar or improved localization accuracy.In some embodiments, the localization service guided by the componentmay reduce the number of iterations run by the pose estimationalgorithms, which may determine a transformation that aligns one subsetof features with another subset of features with matching descriptors,by about ten times, for example, from one hundred iterations to twelveiterations, with similar or improved localization accuracy.

The localization service may be on the XR devices, on the cloud, orboth. In some embodiments, a persisted map may be downloaded to an XRdevice for localizing the XR devices in the map, for example, asdescribed above with respect to FIG. 26 . In some embodiments, an XRdevice may upload information (e.g., 2D features and/or associatedmetadata) to a cloud containing the localization service and receive itslocation in one or more persisted maps, for example, as described abovewith respect to FIGS. 61-63C.

Such a localization service may receive and apply any one or more typesof location metadata in connection with a localization request to selecta set of candidate maps, frame descriptors or other criteria. Thesecriteria may be used to select one or more canonical maps or segments ofa canonical map against which localization may be attempted. Othercriteria, such as deep key frame descriptors, may be used to furtherdown select from the set of candidate maps or to identify segments ofthe candidate maps against which to attempt localization.

Following such down selection, a feature-level comparison may beperformed. A set of features in a key frame generated by the device maybe compared to a set of features in an identified segment of a candidatemap, for example. Where there is more than one candidate map or morethan one identified segment of a candidate map, the feature set from thedevice may be compared to multiple sets of features before alocalization result is determined. The comparisons may continue untilall candidate segments of all candidate maps are processed. Localizationmay be deemed successful if a set of features from a candidate map thatbest corresponds to the feature set from the device has an error belowsome threshold. Alternatively or additionally, processing may completeonce a set of features from a candidate map with a correspondence withan error below some threshold is identified. Regardless, thelocalization process may involve comparing multiple sets of features.For simplicity, processing of one set is described.

The localization service may respond to requests from one or more XRdevices to localize with respect to a set of one or more persisted maps.The request may include 2D features extracted from images of thephysical world around the device. In some embodiments, the images may becaptured by multiple sensors, such as cameras, of the device. In someembodiments, a set of features from an XR device may be based oninformation captured by multiple sensors simultaneously, which mayincrease the speed and/or accuracy of finding matching sets of features.

Those 2D features may be posed relative to coordinate frames used by theXR devices in a way that the 2D features captured by multiple sensorscan be processed together to provide the localization result. In someembodiments, the localization service may integrate 2D features frommultiple sensors by embedding sensor extrinsic parameters in the 2Dfeatures. The sensor extrinsic parameters may include the physicaldimensions of the sensors, the distances between the sensors, thephysical dimensions of an XR device display, the locations of thesensors on the XR device, etc. Appropriate transformations to representthe sensor extrinsic parameters, which may be used to implement theembedding, may be determined through a calibration process.

This set of 2D features may serve as an input to the localizationprocess. The localization service may identify a set of 3D features froma candidate map. The set of 3D features may be selected based on featuredescriptors that match descriptors of the 2D features. The inventorshave recognized and appreciated that some of the matched correspondencesmay be true, meaning that the corresponding features in the 2D and 3Dsets of features represent the same features in the physical world,while others of the matched correspondences may be false, meaning thatthe corresponding features in the 2D and 3D sets of features, despitehaving matching descriptors, do not represent the same features in thephysical world. Accordingly, assessing the quality of thecorrespondence, and weighting the process towards consideration ofcorresponding features of high quality may shorten computing time and/orincrease the accuracy of the localization result.

The localization service may include a component configured to assessthe quality of the corresponding features. The localization service maythen identify a transformation that aligns the 2D set of features to the3D sets of features, using processing on subsets of correspondingfeatures, with inclusion in the subsets weighted towards using pairs ofcorresponding features with high quality.

In some embodiments, searching for a transformation between two sets offeatures may be computationally intensive. In some embodiments, thelocalization service may use gravity orientation to reduce the degreesof freedom that should be searched when matching 2D features to 3Dfeatures. Both the 2D features from the device and the 3D features froma stored map may be expressed in a coordinate frame with one coordinatealigned with gravity. In some embodiments, the localization service may,prior to searching for a transformation, rotate the set of 2D featuressuch that the coordinate of the 2D feature set aligned with gravityaligns with the coordinate of the set of 3D features that is alsoaligned with gravity. Alternatively or additionally, any searching fortransformations may exclude transformations that would change thealignment of the feature set relative to gravity.

From this transformation, the localization service may compute andreturn to the device a transformation to relate its local coordinateframe in which the 2D features were posed to a coordinate frame of thepersisted map from which the set of 3D features was obtained.

The component configured to assess the quality of the matchedcorrespondences may include an artificial neural network. The neuralnetwork may be trained to provide a quality metric for each pair ofcorresponding features. The quality metric may indicate the likelihoodthat a 2D-3D feature pair identify the same feature in the physicalworld. In some embodiments, the quality metric may be a probability inthe range of zero to one, for example, with one indicating a correctmatch and zero indicating a false match.

The neural network may be trained with a data set that has featurepoints represented as both 2D and 3D features. The training set may alsoinclude 2D and 3D features that represent different features in thephysical world but have the same or similar descriptors. Such a data setmay be generated synthetically, such as from computer graphics generateddata depicting environments in which XR devices may operate. The datamay have noise applied to it, indicative of noise that may exist in themeasurement process or be otherwise distorted to be representative ofreal-world data.

Training of the neural network may be based on loss function thatpenalizes wrong results. A wrong result may be either assigning a lowquality to a pair of 2D and 3D features that represent the same featurein the physical world or assigning a high quality to a pair of 2D and 3Dfeatures that represent different features in the physical world.Alternatively or additionally, the loss function may promote correctresults, which may be either assigning a high quality to a pair of 2Dand 3D features that represent the same feature in the physical world orassigning a low quality to a pair of 2D and 3D features that representdifferent features in the physical world.

FIG. 64 is a block diagram illustrating a portion of an XR system 6400that provides large and very large scale localization, according to someembodiments. FIG. 65 is a schematic diagram illustrating informationabout a physical world being processed by the XR system 6400, accordingto some embodiments. Processing as shown in FIG. 64 may be implementedon a portable device or may be distributed across a device and one ormore remote processing systems, such as a cloud service.

The XR system 6400 may include one or more sensors 6402 configured tocapture information 6404 about a physical world. In some embodiments,the sensors may include one or more image sensors, for example, cameras552 and/or 553 in FIG. 5B, which may output grayscale and/or color imageframes at fixed time intervals. In some embodiments, the sensors mayinclude one or more inertial measurement units (IMU), for example, IMU557 in FIG. 5B, which may detect movements and orientations. The imageframes output by the cameras may be appended with orientations capturedby the IMU such as gravity orientations of the device when the imagesare taken. The images may be rotated such that the gravity orientationsare aligned, which reduces the degrees of freedom to search for alocalization service.

FIG. 65 illustrates embedding of data collected by the sensors 6402. Inthe example of FIG. 65 , four cameras cam0-cam3 of an XR device areillustrated. The XR device may have a device coordinate frame 6502. Insome embodiments, the device coordinate frame 6502 may be a keyrigcoordinate frame, indicating a pose of the device when a keyrig (e.g.,keyrig 704 FIG. 7 ) is captured. In some embodiments, the pose of thekeyrig may be indicated in a coordinate frame of a tracking map built onthe XR device, for example, the world coordinate frame 86 (FIGS. 9, 10). In some embodiments, the pose of the keyrig may be determined basedon the location of a display of the device such that virtual content canbe displayed correctly on the display once the device is localized to amap.

The XR device may attempt to localize to one or more maps persisted in adatabase 6410, for example, a canonical map 120 in FIG. 16 . Thepersisted maps may include map points representing 3D features of thephysical world, for example, map points 3802 in FIG. 38A. The map pointsmay share a map coordinate frame and be referable through the shared mapcoordinate frame, for example, a canonical coordinate frame 4806C inFIG. 39A.

The system 6400 may provide a pose estimation 6424, which may be in theform of a transformation between a device coordinate frame and a mapcoordinate frame. In the example of FIG. 65 , an example of the poseestimation 6424 is illustrated as a transformation 6506 between thedevice coordinate frame 6502 and the map coordinate frame 6504. Thetransformation 6506 may include translations and rotations between thecoordinate frames 6502 and 6504, which may be represented by a matrix.

As described above, a pose may be estimated by finding a transformationthat aligns the two sets of features. The XR system 6400 may include afeature extraction component 6406 configured to identify features fromsensor-captured information and output feature information 6408. Asdescribed above, examples of features may include corners and edges ofobjects in a physical world. In the example of FIG. 65 , the featureextraction component 6406 may receive an image 6508 captured by cam3,and identify one or more features 6510 k in the image 5408. The featureextraction component 6406 may also receive images from cam0, cam1, andcam2, and identify features such as 6510 i and 6510 j. Descriptors foreach feature may also be generated to enable efficient matching. In someembodiments, the feature information 6408 may include the descriptors(e.g., DSF descriptors in FIG. 25 ).

The feature extraction component 6406 may also be configured to appendsensor extrinsic parameters to the identified features such that the XRsystem 6400 can simultaneously process information captured by multiplesensors. In some embodiments, the feature information 6408 may include a6d vector by combining two 3d vectors. A first 3d vector may indicatethe position of the feature in a sensor coordinate frame of the sensorthat captured the image containing the feature (e.g., u_(i)v_(i)w_(i),u_(j)v_(j)w_(j), u_(k)v_(k)w_(k)). A second 3d vector may indicate theposition in the device coordinate frame 6502 of the sensor that capturedthe image containing the feature (e.g., t₀, t₁, t₂, t₃). For example,the feature information 6408 for the feature 6510 k may include a uniquedescriptor for the feature, the first vector u_(k)v_(k)w_(k), and thesecond vector t₃.

In some embodiments, the feature extraction component 6406 may be on thedevices. The feature information 6408 may be communicated to alocalization service 6426. Communicating the feature information 6408may consume less bandwidth than sending full images. However, thepresent application is not limited in this regard. In some embodiments,the localization service 6426 may include the feature extractioncomponent 6406.

The localization service 6426 may include a feature matching component6414, which may receive feature information 6408 from the featureextraction component 6406. The feature matching component 6414 may alsoreceive map information 6412 from one or maps persisted in the database6410. The one or more maps may be selected from the database 6410 by themethod of FIG. 28 and/or the method 900 of FIG. 29 . The map information6412 may include map points representing 3D features of the physicalworld. The feature matching component 6414 may be configured to matchcorresponding features from feature information 6408 and map information6412 by, for example, selecting pairs of features from the 2D and 3Dsets that have the same or similar descriptors.

In the example of FIG. 65 , the feature matching component 6414 maydetermine that features 6510 i, 6510 j, 6510 k match with map points6512 i, 6512 j, 6512 k, respectively. The map points 6512 i, 6512 j,6512 k may be expressed as 3d vectors x_(i)y_(i)z_(i), x_(j)y_(j)z_(j),x_(k)y_(k)z_(k), indicating the positions of the map points in the mapcoordinate frame 6504. As illustrated, some matched correspondences maybe correct, for example, the correspondence between 6510 j and 6512 j,and the correspondence between 6510 k and 6512 k; some matchedcorrespondences may be false, for example, the correspondence between6510 i and 6512 i.

The localization service 6426 may include a matched correspondencesquality prediction component 6418 configured to assess the quality ofthe pairs of matched features 6416 provided by the feature matchingcomponent 6414. The matched correspondences quality prediction component6418 may provide quality information 6420 to a pose estimation componentof the localization service 6426 such that the pose estimation component6422 can be guided by the quality information 6420 when computing thepose estimation 6424.

The quality information 6420 may include quality metrics for each pairof matched features. In some embodiments, the quality metric may be aprobability in the range of zero to one, with a higher assigned qualityindicating a higher likelihood that the matched features represent thesame location in the physical world. It should be appreciated that acomponent may be configured to indicate the quality of the matchedcorrespondences with other numeric values, for example, indicating alikely correct match with zero and likely false match with one, or viceversa.

The pose estimation component 6422 may process the pairs of matchedfeatures 6416, output by the feature matching component 6414, based onthe quality information 6420, output by the matched feature qualitypredication component 6418. In some embodiments, the pose estimationcomponent 6422 may select a first subset of pairs of matched featuresfrom the set 6416, and compute a first pose based on the selectedsubset. The number of correspondences in the subset may be five, seven,eight, or any value that is sufficient to provide a valid transformationmatrix. The selection of the first subset may be guided by the qualityinformation 6420, with the selection weighted towards higher qualitymatching features. For example, the pose estimation component 6422 mayrandomly select the first subset of matching features from all pairs ofmatching features, but with a biased selection such that matchingfeatures having higher quality metrics are more likely to be selectedthan those with low quality metrics. The likelihood of a pair of matchedfeatures being selected may be proportional to the quality metric.

The pose estimation component 6422 may compute a transformation thataligns the 2D features of the selected subset of pairs of matchedfeatures with corresponding 3D features in the subset. In systems thatconsider gravity, transformations that alter the orientation of the 2Dfeatures relative to gravity may not be considered.

The pose estimation component 6422 may determine the accuracy of thefirst pose by applying the computed transformation to a larger set ofpairs of matched features, including some or all of the pairs of matchedfeatures not included in the first subset. In some embodiments, the poseestimation component 6422 may determine accuracy by computing projectionerrors for individual correspondences. A projection error for a matchedcorrespondence may indicate a distance between the position of the 2Dfeature in the image containing the 2D feature and the position of thematched map point projected to a plane that the image extends.

In some scenarios, pose estimation component 6422 may compute furtherestimated poses to ensure an accurate pose is computed. In someembodiments, the pose estimation component 6422 may determine theestimated pose is accurate when the number of pairs of matched featureshaving projection errors below a threshold error (e.g., two pixels) isabove a threshold value (e.g., 80% of the set 6416). This check may bemade as the first pose estimation is completed. Alternatively, the poseestimation component 6422 may compute a plurality of pose estimationsand check the accuracy of the best of the pose estimations relative to athreshold value.

When the pose estimation component 6422 determines that the firstlocalization is not sufficiently accurate, or in embodiments in whichmultiple pose estimations are computed before assessing the suitabilityof the best one, the pose estimation component 6422 may randomly selecta second subset of pairs of matched features from the set 6416. Theselection of a further subset of pairs of matched features, as for thefirst subset, may be weighted by the quality information 6420. The poseestimation component 6422 may compute a second poses to align the 2Dfeatures of the selected second subset and the 3D features. This processmay be continued, with additional subsets being selected and processed,until an accurate localization result is found, and/or other stopconditions are reached such as processing of a predetermined number ofsubsets. It should be appreciated that with the guidance from thequality information 6420, a pose estimation component may be able toprovide an accurate localization result with selecting and computingbased on ten times fewer number of subsets of matched correspondencesthan without the guidance.

In some embodiments, the matched feature quality predication component6418 may include an artificial neural network 6602, for example, asillustrated in a subsystem 6600 of the XR system 6400 in FIG. 66 ,according to some embodiments. The neural network 6602 may include anencoding layer 6604, a decoding layer 6616, and an intermediate layer6622 between the encoding layer 6604 and the decoding layer 6616.

The encoding layer 6604 may include multi-layer perceptrons (MLP) 6606.Each MLP 6606 in the encoding layer 6604 may receive one or more pairsof matched features from the set 6416. In some embodiments, a matchedpair of features may include a 9d vector, which may be a combination ofthe 6d vector of a 2D feature as described above (e.g., u_(k)v_(k)w_(k)& t3) and the 3d vector indicating the position of the matched 3Dfeature in the map (e.g., x_(k)y_(k)z_(k)). The MLPs may provide encodedvectors 6608, which may provide a higher dimensionality of features thanthe input correspondence. In some embodiments, the encoded vectors 6608may include a 64d vector.

The intermediate layer 6622 may include one or more residual networkblocks 6610 connected by element-wise summation blocks 6614. A residualnetwork block 6610 may include MLPs, and a sub-block configured tonormalize a distribution of outputs of the MLPs of the residual networkblock.

The decoding layer 6616 may also include MLPs. Each MLP in the decodinglayer 6616 may receive encoded output from the intermediate layer 6622,and output a decoded vector 6618, which may have a same dimension as theinput correspondence. The decoded vectors 6618 may be converted into thequality information 6420 through activation blocks 6620.

FIG. 67 is a flow chart illustrating a method 6700 of generating areference dataset for training the neural network 6602, according tosome embodiments. The method 6700 may start by creating (Act 6702) areference dataset comprising 2D-3D feature correspondences. Thereference dataset may be synthetic or real-world data. In someembodiments, the reference data set may include pairs of features thatcorrespond to correct matches and those that correspond to incorrectmatches.

At Act 6704, the method 6700 may compute a pose based on the referencedataset by, for example, using the pose estimation component 6420. AtAct 6706, the estimated pose may be used to compute ground truth (GT)projection errors for individual correspondences. In scenarios in whichsynthetic data is used, ground truth may be determined from anytransformation between the 2D and 3D sets introduced in forming thesynthetic data. For other data sets, ground truth may be determined inother ways, including as a result of manual review of data sets or theimages from which the data sets were generated. For a 2D-3D featurecorrespondence, a GT projection error may indicate an actual distancebetween the position of the 2D feature of the correspondence on an imagecontaining the 2D feature and the position of a 2D feature thatcorresponds to the 3D feature in the correspondence.

At Act 6708, the method 6700 may compute GT weights for thecorrespondences of the reference dataset based on the computed GTprojection errors. In some embodiments, the GT weights W_(GT) may bedefined as the Cauchy weight function of the GT projection error asshown below:

$W_{GT} = \frac{C^{2}}{C^{2} + {error}^{2}}$where C may have the value of the error for a GT weight of 0.5. In someembodiments, the training dataset 6710 may include the 2D-3D featurecorrespondences of the reference dataset and the computed GT weights.

FIG. 68 is a flow chart illustrating a method 6800 of training theneural network 6602, according to some embodiments. The method 6800 maystart by computing (Act 6802) quality metrics for each pair of featuresin the training dataset using the neural network 6602. At Act 6804, themethod 6800 may compute a loss based on the difference between thequality metrics and the GT weights. In some embodiments, the loss may bea regression loss on GT weights W_(GT), for example, a mean square errorbetween the quality metrics and corresponding GT weights for the pair offeatures in the training set 6710. At Act 6806, the method 6800 maymodify the weights of the neural network 6602 based on the loss so as toreduce the loss. Pairs of matching features in the training set may beapplied successively, updating the neural network as each pair isprocessed, so as to decrease the loss.

Regardless of the specific method by which the neural network istrained, once trained, it may be used to guide the selection of subsetsof the matching pairs of features to reduce the number of subsetsprocessed to identify a pose between two sets of features, as describedabove in connection with FIG. 64 . This process may be preceded orfollowed by other processing. One or more criteria may be applied beforethe processing illustrated in FIG. 64 to determine which sets of 3Dfeatures to compare to a set of 2D features. If multiple sets of 3Dfeatures are to be compared, the process of FIG. 64 may be repeated foreach set of 3D features to determine the best matching feature set.Following the processing of FIG. 64 , identified pose may be convertedto a localization result.

The process for pose estimation of FIG. 64 alternatively or additionallymay be applied in other contexts. For example in merging a tracking mapto a canonical map, a set of 2D features from the tracking map, such asthose associated with a persistent pose, may be compared to 3D featuresin the canonical map. The pose estimation process of FIG. 64 may beapplied to determine whether sets of features match and, if so, atransformation between the sets of features. Computation of thattransformation may be used to align the tracking map to the canonicalmap so that the maps may be combined.

Accordingly, it should be appreciated that the process of poseestimation as described herein in connection with localizing a devicemay be applied in other scenarios.

Gravity Preserving Map Merging

As described above, a set of canonical maps of the environment in whichmultiple XR devices operate may provide an enhanced XR experience tomultiple users using different computing devices (such as, portabledevices including smart phones and XR devices) by enabling their localrepresentations of the world (e.g., in the form of tracking maps) to betransformed to the same coordinate frame. By localizing to the sameframe of reference, all of the devices may present to their usersvirtual content in the same location relative to the physical world,creating a more realistic and immersive shared user experience.

The canonical maps may be created by incrementally merging tracking mapsreceived from the users' devices to create a virtual representation ofthe world. In some embodiments, users' devices may send their trackingmaps to a cloud-based service to merge with map(s) of the environmentpreviously stored in the cloud-based service. The map merge component ofthe cloud service may attempt to identify a portion of each new trackingmap that represents the same region of the physical world as a portionof an existing canonical map. This identification may be made in steps,with an initial step involving finding likely regions of overlap bymatching location metadata associated with regions of the already storedcanonical maps to similar location metadata associated with the trackingmap. Alternatively or additionally, descriptors computed for key framesin the tracking map may be matched to similar descriptors associatedwith portions of the canonical map. These likely regions of overlapserve as candidates for further processing.

These candidate regions of overlap may be further processed to identifyfeature points in the tracking map with corresponding feature points inthe canonical map. Corresponding features may be identified based oncharacteristics of the features. For example, corresponding features maybe identified based on similar descriptors, such as DSF descriptorsdescribed above, being assigned to features in the tracking map and thecanonical map.

Further steps in the map merge process may entail determining atransformation between the sets of corresponding features that providesa suitably low error between the positions of the transformed featuresin a first set of features derived from the tracking map and a secondset of features derived from the canonical map. One or more errormetrics may be applied to compare error to a threshold. For example, thenumber or percentage of feature points in the tracking map that, aftertransformation, are within a threshold distance of their correspondingfeature point in the canonical map. Alternatively or additionally, errormay be measured based on distance between transformed feature pointsfrom the tracking map and their corresponding features in the canonicalmap. As a specific example, an error measure may be the root meansquared distance for the set of corresponding feature points.

Multiple transformations may be attempted until a transformation withsuitable error is identified. Guided search strategies may be used toselect the transformations tested during a search for a transformationthat provides a low error meeting an ending criteria for the search. Forexample, searching for a suitable transformation may entail performing apose estimation on weighted subsets of pairs of matching features andthen checking the error when the estimated pose is applied as atransformation to the points of the tracking map. Such a search may usetechniques as described above in connection with FIG. 64 .

Regardless of the specific search strategy, if the search process isconcluded with no suitable transformation identified, the merge processmay continue with selecting another candidate region of one of thecanonical maps to compare to the same region of the tracking map.Alternatively or additionally, a different region of the tracking mapmay be selected for comparison to candidate regions of the canonicalmaps in an attempt to find a transformation with suitably low error.

If all candidate regions are searched without identifying a suitabletransformation, the tracking map may not be combined with any of theexisting maps. One or more other actions might be taken, such as endingthe merge process with no action. In some embodiments, the tracking mapmay be deemed to represent a portion of the physical world for which nocanonical map already exists, and the tracking map may be stored as anew canonical map in the set of canonical maps. In this way, an initialcanonical map inherits properties from the tracking map, including itsorientation with respect to gravity, as described above.

Conversely, the ability to determine a transformation with suitably lowerror serves as confirmation that the candidate region likely representsthe same region of the physical world as the tracking map and the twomaps can be merged. In some embodiments, identification of a suitabletransformation for merging two maps may entail additional processing,such as finding multiple portions of the tracking map that can bealigned, with sufficiently close transformations. For example, atracking map may be merged with a canonical map upon finding multiplekey frames in the tracking map that have similar poses with respect tothe canonical map. In that scenario, the determined transformation maybe a combination of the transformations determined for the matchingregions, such as an average of the transformations.

Regardless of the specific approach by which a suitable transformationis determined, that transformation may be applied to the entire trackingmap, which may align the tracking map to the canonical map. Overlappingpieces may be combined, such as by averaging or aggregating featurepoints in the two maps or overwriting feature points of the canonicalmaps with feature points from the new map. Non-overlapping portions ofthe tracking map may be appended to the canonical map.

The inventors have recognized that errors may be introduced during themap merging process when there are errors in the determinedtransformation between a tracking map received from the user's deviceand a canonical map that the tracking map is combined with. As a resultof errors in the transformations, the tracking maps as merged into thecanonical maps may be skewed, causing the map merging process to outputa skewed merged map. Such alignment failures may be caused for diversereasons, such as, confusing visual similarities between feature points.The inventors have developed a merging technique (performed by map mergeportion 810 of FIG. 26 , for example) that reduces merging errors byensuring that, after applying a determined transformation, a gravitydirection of the tracking map aligns with a gravity direction of thecanonical map with which the tracking map is to be merged.

In some embodiments, the gravity direction of a tracking map is obtainedor derived from an inertial measurement unit (e.g., IMU 557) included inthe portable device. As a specific example, the gravity direction of thetracking map may be obtained or derived from one or more accelerometers(e.g., 3D accelerometers) included in one or more IMUs. The tracking mapmay be generated at the portable device based on sensor data collectedfrom various sensors on the portable device, including for examplecameras that are mechanically coupled to the same support structure asthe one or more IMUs. As images are output from the cameras, they may betransformed so as to have a predetermined orientation with respect togravity. As features from these images are combined into a tracking map,those features will have the same orientation with respect to gravity,such that all generated tracking maps may have the same alignment withrespect to gravity.

As all tracking maps may have the same alignment with respect togravity, any tracking map sent to the cloud for map merge processingwill be aligned with respect to gravity. When a tracking map does notmatch any existing canonical map and is used as the start of a newcanonical map, that canonical map may inherit properties from thetracking map, including its orientation with respect to gravity.Accordingly, the canonical maps may each start aligned with gravity. Insystems in which canonical maps are initiated other than from a trackingmap, the canonical map formation process may be restricted otherwise tostart each canonical map aligned with gravity.

In some scenarios, as a result of an error in a determinedtransformation, the tracking map as transformed by the cloud-basedservice to align a set of features in the tracking map with matchingfeatures in the canonical map may be skewed such that it no longeraligns with gravity. Despite a low value of an error metric derived fromcloseness of match between features, such a condition signals an errorhas occurred in the alignment process.

By enforcing a gravity constraint such that transformed tracking mapsthat are skewed with respect to gravity are not used for merging, theaccuracy of merged maps is improved. Such a constraint may beimplemented in the map merging process by comparing the gravitydirection of the transformed tracking map with a gravity direction of anenvironment map in the cloud, and merging the two maps only when thegravity directions align. This constraint may be applied at one or morestages in the map merge process. For example, once a transformation isdetermined from aligning matching feature points, the effect of thattransformation on the orientation of the tracking map with respect togravity may be assessed. A transformation that rotates the tracking mapwith respect to gravity by more than a threshold amount may bediscarded, and the process of searching for a suitable transformation toalign the tracking map with a portion of a canonical map may be resumedor may end without merging the tracking map. Alternatively oradditionally, as part of a search for a suitable transformation in whichcandidate transformations are tested on subsets of features, onlycandidate transformations that do not rotate the subset of features withrespect to gravity may be considered.

FIG. 69 illustrates a map merge process that ensures that a gravitydirection of a tracking map aligns with a gravity direction of anenvironment map with which it is to be merged. As illustrated, both thetracking maps received from portable devices and environment maps,serving as a set of canonical maps for localization and merging, areinitially aligned with respect to gravity. Such an alignment, forexample, may be achieved by aligning with gravity one coordinate of thecoordinate frames used within those maps.

The map merge process 6900 begins with a map merge portion (e.g., mapmerge portion 810 of FIG. 26 ) receiving a tracking map (e.g., a newtracking map) from a portable device and one or more environment mapsstored in a database in the cloud. Receiving the tracking map mayinclude receiving data associated with the tracking map (e.g., sets offeature points and/or other information described herein), where thedata is organized with respect to gravity. The tracking map may berepresented by a coordinate system, where one of the coordinates of thecoordinate system is the direction of gravity.

As described herein, the one or more environment maps may be selectedfrom the database as candidates for comparison to the tracking map. Thisselection may be based on area identifiers, area attributes, or othermetadata associated with the new tracking map. In some embodiments, theenvironment maps may be derived by filtering a larger universe ofcanonical maps based on one or more criteria including, for example, ageographic location of the new tracking map, similarity of one or moreidentifiers of network access points associated with the new trackingmap and the environment maps, similarity of metrics representingcontents of the new tracking map and the environment maps, and degree ofmatch between a portion of the new tracking map and portions of theenvironment maps. The one or more environment maps received by the mapmerge portion may include a filtered set of one or more environmentmaps.

In some embodiments, the one or more candidate environment maps may beranked, for example by a map rank portion (e.g., map rank portion 806 ofFIG. 26 ), for use in selecting the environment maps to merge with thenew tracking map. The environment maps may be ranked by relevance. Theprocess 6900 may be performed on the candidate maps one at a time,starting with the highest ranking map. If a suitable transformationbetween that map and the tracking map is found, the new tracking map maybe merged with the selected candidate environment map to create one ormore updated/merged environment maps as described herein. If no suitabletransformation is found, the process 6900 may be repeated with the nexthighest ranked candidate map. In embodiments in which maps are segmentedinto areas, such as tiles, the process 6900 map operate on tiles orother portions of maps.

As shown in FIG. 69 , at act 6910 the tracking map and the selectedenvironment map are aligned. As described above, alignment may entailsearching for a transformation between features in the new tracking mapand the selected environment map that have been determined tocorrespond, such as a result of having matching identifiers. Followingact 6910, the determined transformation may be applied to the trackingmap at act 6912 such that it is aligned with the selected environmentmap. At act 6920, a determination is made as to whether the alignedtracking map transformed in act 6912 aligns with a gravity direction ofthe environment map. The process 6900 may branch at decision block 6922,depending on whether the transformed tracking map has a valid alignmentwith respect to gravity. A valid alignment may be identified, forexample, based on a rotation, less than a threshold amount, between thegravity direction of the tracking map and the gravity direction of theenvironment map.

In response to a determination that the gravity directions of the twomaps align with one another, the maps may be merged in act 6930. Theresulting merged map retains its orientation with respect to gravity.

In response to a determination, in act 6920, that the gravity directionsof the two maps do not align with one another, or other indication thatthe gravity direction of the transformed tracking map is invalid, themap merge process refrains from merging the new tracking map with theenvironment map, in act 6930. Rather, in response to a determination, inact 6920, that the gravity direction of the transformed tracking map isinvalid, process 6900 may branch from decision block 6922 back to act6910 where further transformations are attempted to determine whetherthe gravity directions of the two maps can be aligned. The process 6900may continue looping in this way until a valid alignment is found orsome stop condition on the process is reached. That stop condition maybe, for example, based on the passage of time, attempting apredetermined number of transformations, or other conditions.

Upon reaching such a stop condition, one or more actions may beperformed. In some embodiments, the map merge process 6900 may beperformed for each environment map in the set of candidate environmentmaps until an environment map is located that can be aligned with thetransformed tracking map while preserving the orientation of thetracking map with respect to gravity. The new tracking map may then bemerged with the located environment map. Alternatively, if no suchcandidate map is identified, other actions may be taken, such as savingthe tracking map as a new canonical map, or ending the merge processwithout updating the stored set of canonical maps based on the newtracking map.

FIG. 69 illustrates a merge process in which a tracking map is mergedwith an environment map selected from a database of canonical maps.Similar processing may be applied to other map merge operations. Forexample, as canonical maps in the database expand, such as throughmerging with new tracking maps, the canonical maps may grow such thattwo or more maps in the database represent overlapping regions of thephysical world. In some embodiments, a process as illustrated in FIG. 69may be applied with selected maps from the database as inputs in placeof a tracking map so that overlapping canonical maps may be merged.Regardless of the specific maps that are input to the merge process, theprocess may be implemented so as to preserve the orientation of the mapswith respect to gravity, such as by refraining from merging maps basedon an alignment that changes the orientation with respect to gravity ofthe map to be merged.

Further Considerations

FIG. 60 shows a diagrammatic representation of a machine in theexemplary form of a computer system 1900 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed, according to someembodiments. In alternative embodiments, the machine operates as astandalone device or may be connected (e.g., networked) to othermachines. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

The exemplary computer system 1900 includes a processor 1902 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 1904 (e.g., read only memory (ROM), flash memory,dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) orRambus DRAM (RDRAM), etc.), and a static memory 1906 (e.g., flashmemory, static random access memory (SRAM), etc.), which communicatewith each other via a bus 1908.

The computer system 1900 may further include a disk drive unit 1916, anda network interface device 1920.

The disk drive unit 1916 includes a machine-readable medium 1922 onwhich is stored one or more sets of instructions 1924 (e.g., software)embodying any one or more of the methodologies or functions describedherein. The software may also reside, completely or at least partially,within the main memory 1904 and/or within the processor 1902 duringexecution thereof by the computer system 1900, the main memory 1904 andthe processor 1902 also constituting machine-readable media.

The software may further be transmitted or received over a network 18via the network interface device 1920.

The computer system 1900 includes a driver chip 1950 that is used todrive projectors to generate light. The driver chip 1950 includes itsown data store 1960 and its own processor 1962.

While the machine-readable medium 1922 is shown in an exemplaryembodiment to be a single medium, the term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” shall also be taken to include any medium thatis capable of storing, encoding, or carrying a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present invention. The term“machine-readable medium” shall accordingly be taken to include, but notbe limited to, solid-state memories, optical and magnetic media, andcarrier wave signals.

Having thus described several aspects of some embodiments, it is to beappreciated that various alterations, modifications, and improvementswill readily occur to those skilled in the art.

As one example, embodiments are described in connection with anaugmented (AR) environment. It should be appreciated that some or all ofthe techniques described herein may be applied in an MR environment ormore generally in other XR environments, and in VR environments.

As another example, embodiments are described in connection withdevices, such as wearable devices. It should be appreciated that some orall of the techniques described herein may be implemented via networks(such as cloud), discrete applications, and/or any suitable combinationsof devices, networks, and discrete applications.

Further, FIG. 29 provides examples of criteria that may be used tofilter candidate maps to yield a set of high ranking maps. Othercriteria may be used instead of or in addition to the describedcriteria. For example, if multiple candidate maps have similar values ofa metric used for filtering out less desirable maps, characteristics ofthe candidate maps may be used to determine which maps are retained ascandidate maps or filtered out. For example, larger or more densecandidate maps may be prioritized over smaller candidate maps.

Such alterations, modifications, and improvements are intended to bepart of this disclosure, and are intended to be within the spirit andscope of the disclosure. Further, though advantages of the presentdisclosure are indicated, it should be appreciated that not everyembodiment of the disclosure will include every described advantage.Some embodiments may not implement any features described asadvantageous herein and in some instances. Accordingly, the foregoingdescription and drawings are by way of example only.

The above-described embodiments of the present disclosure can beimplemented in any of numerous ways. For example, the embodiments may beimplemented using hardware, software or a combination thereof. Whenimplemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. Such processorsmay be implemented as integrated circuits, with one or more processorsin an integrated circuit component, including commercially availableintegrated circuit components known in the art by names such as CPUchips, GPU chips, microprocessor, microcontroller, or co-processor. Insome embodiments, a processor may be implemented in custom circuitry,such as an ASIC, or semicustom circuitry resulting from configuring aprogrammable logic device. As yet a further alternative, a processor maybe a portion of a larger circuit or semiconductor device, whethercommercially available, semi-custom or custom. As a specific example,some commercially available microprocessors have multiple cores suchthat one or a subset of those cores may constitute a processor. Though,a processor may be implemented using circuitry in any suitable format.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer. Additionally, acomputer may be embedded in a device not generally regarded as acomputer but with suitable processing capabilities, including a PersonalDigital Assistant (PDA), a smart phone or any other suitable portable orfixed electronic device.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets. As another example, a computer may receiveinput information through speech recognition or in other audible format.In the embodiment illustrated, the input/output devices are illustratedas physically separate from the computing device. In some embodiments,however, the input and/or output devices may be physically integratedinto the same unit as the processor or other elements of the computingdevice. For example, a keyboard might be implemented as a soft keyboardon a touch screen. In some embodiments, the input/output devices may beentirely disconnected from the computing device, and functionallyintegrated through a wireless connection.

Such computers may be interconnected by one or more networks in anysuitable form, including as a local area network or a wide area network,such as an enterprise network or the Internet. Such networks may bebased on any suitable technology and may operate according to anysuitable protocol and may include wireless networks, wired networks orfiber optic networks.

Also, the various methods or processes outlined herein may be coded assoftware that is executable on one or more processors that employ anyone of a variety of operating systems or platforms. Additionally, suchsoftware may be written using any of a number of suitable programminglanguages and/or programming or scripting tools, and also may becompiled as executable machine language code or intermediate code thatis executed on a framework or virtual machine.

In this respect, the disclosure may be embodied as a computer readablestorage medium (or multiple computer readable media) (e.g., a computermemory, one or more floppy discs, compact discs (CD), optical discs,digital video disks (DVD), magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement the various embodiments ofthe disclosure discussed above. As is apparent from the foregoingexamples, a computer readable storage medium may retain information fora sufficient time to provide computer-executable instructions in anon-transitory form. Such a computer readable storage medium or mediacan be transportable, such that the program or programs stored thereoncan be loaded onto one or more different computers or other processorsto implement various aspects of the present disclosure as discussedabove. As used herein, the term “computer-readable storage medium”encompasses only a computer-readable medium that can be considered to bea manufacture (i.e., article of manufacture) or a machine. In someembodiments, the disclosure may be embodied as a computer readablemedium other than a computer-readable storage medium, such as apropagating signal.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of the present disclosure asdiscussed above. Additionally, it should be appreciated that accordingto one aspect of this embodiment, one or more computer programs thatwhen executed perform methods of the present disclosure need not resideon a single computer or processor, but may be distributed in a modularfashion amongst a number of different computers or processors toimplement various aspects of the present disclosure.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks or implement particularabstract data types. Typically the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconveys relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Various aspects of the present disclosure may be used alone, incombination, or in a variety of arrangements not specifically discussedin the embodiments described in the foregoing and is therefore notlimited in its application to the details and arrangement of componentsset forth in the foregoing description or illustrated in the drawings.For example, aspects described in one embodiment may be combined in anymanner with aspects described in other embodiments.

Also, the disclosure may be embodied as a method, of which an examplehas been provided. The acts performed as part of the method may beordered in any suitable way. Accordingly, embodiments may be constructedin which acts are performed in an order different than illustrated,which may include performing some acts simultaneously, even though shownas sequential acts in illustrative embodiments.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

Also, the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having,” “containing,” “involving,” andvariations thereof herein, is meant to encompass the items listedthereafter and equivalents thereof as well as additional items.

What is claimed is:
 1. A method of operating a cross reality system inwhich one or more environment maps are stored in a database and arepresentation of a physical environment computed based on sensor datacollected by a device worn by a user, the method comprising: receiving arepresentation of a physical environment from the device, wherein therepresentation of the physical environment is aligned with respect to agravity direction; determining a transformation between therepresentation of the physical environment and an environment map;determining whether to modify the representation of the physicalenvironment with the environment map, wherein determining whether tomodify comprises determining whether applying the transformation to therepresentation of the physical environment produces a transformedrepresentation of the physical environment that aligns with respect tothe gravity direction; and modifying the environment map based on therepresentation of the physical environment based at least in part ondetermining that the transformed representation of the physicalenvironment aligns with the gravity direction.
 2. The method of claim 1,wherein determining whether applying the transformation to therepresentation of the physical environment produces the transformedrepresentation of the physical environment that aligns with respect tothe gravity direction comprises: determining whether applying thetransformation to the representation of the physical environmentproduces the transformed representation of the physical environment thatis rotated with respect to the gravity direction by more than athreshold amount; in response to determining that applying thetransformation to the representation of the physical environment doesnot produce the transformed representation of the physical environmentthat is rotated with respect to the gravity direction by more than thethreshold amount, selecting the transformation as the determinedtransformation; and in response to determining that applying thetransformation to the representation of the physical environmentproduces the transformed representation of the physical environment thatis rotated with respect to the gravity direction by more than thethreshold amount, discarding the transformation.
 3. The method of claim1, further comprising: identifying a set of environment maps from thedatabase to be modified with the representation of the physicalenvironment; and for each environment map in the set of environmentmaps: determining the transformation between the representation of thephysical environment and the environment map; determining whether tomodify the environment map with the representation of the physicalenvironment; and modifying the environment map with the representationof the physical environment based on determining that the transformedrepresentation of the physical environment aligns with the gravitydirection.
 4. The method of claim 3, further comprising, for eachenvironment map in the set of environment maps: refraining frommodifying the environment map with the representation of the physicalenvironment based on determining that a gravity direction of theenvironment map does not align with the gravity direction of thetransformed representation of the physical environment.
 5. The method ofclaim 3, wherein identifying the set of environment maps comprises:determining area identifiers associated with the representation of thephysical environment; and identifying the set of environment maps fromthe database based, at least in part, on the area identifiers associatedwith the representation of the physical environment.
 6. The method ofclaim 3, wherein identifying the set of environment maps from thedatabase further comprises: filtering the set of environment maps basedon similarity of one or more metrics associated with the representationof the physical environment and in the set of environment maps.
 7. Acomputing device configured for use in a cross reality system in which aportable device operating in a three-dimensional (3D) environmentrenders virtual content, the computing device comprising: at least oneprocessor; a computer-readable medium connected to the processor; aplurality of environment maps stored in the computer-readable medium;and computer-executable instructions configured to, when executed by theat least one processor, perform a method comprising: receiving arepresentation of the physical environment from the portable device,wherein the representation of the physical environment is aligned withrespect to a gravity direction; determining whether to merge anenvironment map with the representation of the physical environment,wherein determining whether to merge comprises searching for atransformation of the representation of the physical environment thataligns a transformed representation of the physical environment and theenvironment map in a manner that preserves alignment of the transformedrepresentation of the physical environment with respect to the gravitydirection; and merging the environment map with the transformedrepresentation of the physical environment based on determining that agravity direction of the environment map aligns with the gravitydirection of the transformed representation of the physical environment.8. The computing device of claim 7, wherein searching for atransformation comprises searching for the transformation that aligns afirst set of features associated with the representation of the physicalenvironment with a second set of features associated with theenvironment map with a metric of error below a threshold.
 9. Thecomputing device of claim 8, wherein searching for a transformationcomprises searching for the transformation that does not change anorientation of the transformed representation of the physicalenvironment with respect to the gravity direction.
 10. The computingdevice of claim 7, wherein searching for a transformation comprises:determining whether applying the transformation to the representation ofthe physical environment produces the transformed representation of thephysical environment that is rotated with respect to the gravitydirection by more than a threshold amount; in response to determiningthat applying the transformation to the representation of the physicalenvironment does not produce the transformed representation of thephysical environment that is rotated with respect to the gravitydirection by more than the threshold amount, applying the transformationto the representation of the physical environment to generate thetransformed representation of the physical environment; and in responseto determining that applying the transformation to the representation ofthe physical environment produces the transformed representation of thephysical environment that is rotated with respect to the gravitydirection by more than the threshold amount, discarding thetransformation.
 11. The computing device of claim 7, wherein the methodfurther comprises: identifying a set of environment maps from theplurality of environment maps to be merged with the representation ofthe physical environment; and for each environment map in the set ofenvironment maps: determining whether to merge the environment map withthe representation of the physical environment; and merging theenvironment map with the transformed representation of the physicalenvironment based on determining that a gravity direction of theenvironment map aligns with the gravity direction of the transformedrepresentation of the physical environment.
 12. The computing device ofclaim 11, wherein the method further comprises, for each environment mapin the set of environment maps: refraining from merging the environmentmap with the transformed representation of the physical environmentbased on determining that the gravity direction of the environment mapdoes not align with the gravity direction of the representation of thephysical environment.
 13. The computing device of claim 12, whereinidentifying the set of environment maps further comprises: determiningarea identifiers associated with the representation of the physicalenvironment; and identifying the set of environment maps based, at leastin part on, on the area identifiers associated with the representationof the physical environment.
 14. The computing device of claim 13,wherein identifying the set of environment maps further comprises:filtering the set of environment maps based on similarity of one or moremetrics associated with the representation of the physical environmentand the environment maps in the set of environment maps.
 15. A cloudcomputing environment for an augmented reality system configured forcommunication with a plurality of user devices comprising sensors,comprising: a map database storing a plurality of environment mapsconstructed from data supplied by the plurality of user devices; andnon-transitory computer storage media storing computer-executableinstructions that, when executed by at least one processor in the cloudcomputing environment, perform a method comprising: receiving arepresentation of the physical environment from a user device, whereinthe representation of the physical environment is aligned with respectto a gravity direction; updating the map database based on the receivedrepresentation of the physical environment, wherein updating the mapdatabase comprises, for an environment map in a set of environment maps:determining a transformation between the representation of the physicalenvironment and the environment map; determining whether to merge theenvironment map with the representation of the physical environment,wherein determining whether to merge comprises determining whetherapplying the transformation to the representation of the physicalenvironment produces a transformed representation of the physicalenvironment that aligns with respect to the gravity direction; andmodifying the environment map with the representation of the physicalenvironment based on determining that the transformed representation ofthe physical environment aligns with the gravity direction.
 16. Thecloud computing environment of claim 15, wherein determining thetransformation comprises, for corresponding features in therepresentation of the physical environment and the environment map,selecting as the determined transformation the transformation thataligns the corresponding features with a metric of error below athreshold.
 17. The cloud computing determining the correspondingenvironment of claim 16, wherein the method further comprisesdetermining the corresponding features based on similarity ofidentifiers assigned to the features.
 18. The cloud computingenvironment of claim 16, wherein selecting as the determinedtransformation further comprises selecting the transformation that whenapplied produces the transformed representation of the physicalenvironment that aligns with respect to the gravity direction.
 19. Thecloud computing environment of claim 15, wherein determining thetransformation comprises applying a plurality of candidatetransformations to the representation of the physical environment andselecting as the determined transformation a candidate transformation ofthe plurality of candidate transformations.
 20. The cloud computingenvironment of claim 19, wherein selecting the determined transformationfurther comprises selecting as the determined transformation thecandidate transformation that when applied produces the transformedrepresentation of the physical environment that aligns with respect tothe gravity direction.